IMS AccessPoint - November 2012

Volume 3, Issue 5 • November 2012

AccessPoint

News, views and insights from leading experts in HEOR and RWE

Real-world evidence accelerates research

Collaborating with stakeholders to enhance outcomes Rising to the unique challenges of oncology Renée Arnold finds new demands for HEOR models in PageOUTCOMES 1 - Issue 1 Asian markets Page 30

Phil McEwan shows Murray Aitken reveals key role of IMS CORE savings of half a Diabetes Model trillion dollars, while 1 IMS HEALTH ECONOMICS AND OUTCOMES RESEARCH in HTA submissions outcomesPage are improved Page 37 Page 50

THE MAGAZINE OF IMS HEOR & REAL-WORLD EVIDENCE SOLUTIONS

Volume 3, Issue 5 • November 2012

AccessPoint

News, views and insights from leading experts in HEOR and RWE Seamless integration of patient-level data Industry and academic experts discuss the new opportunities with IMS PharMetrics Plus™ - the largest US claims database page 6

Measuring quality for improved outcomes Shift in focus to quality metrics brings potential for enhanced relationships page 16

Sample size in real-world research Why observational studies require special consideration page 21

Understanding global trends in HTA How to leverage HTA data for an optimal HEOR strategy page 42

IMS HEOR & REAL-WORLD EVIDENCE SOLUTIONS

“We see a marketplace moving closer to greater consensus and partnership.”

WELCOME

CONTENTS

Welcome to the latest issue of ACCESSPOINT, a twice-yearly resource of news, views and insights from IMS Health – offering a current perspective on the changes and exciting advances that are driving more and better health economics and outcomes research.

NEWS

Today, more than ever, countries and healthcare systems worldwide need to focus on strategies to raise efficiency, quality and value against the real pressures of constrained budgets. We are beginning to observe and forecast a significant shift towards incentive-aligned programs (eg, pay-for-performance), rewarding outcomes on the basis of quality metrics and signaling a new role for HEOR in establishing the benchmarks and measures that will deliver the greatest benefits (page 16). We see policy makers, regulators, providers and payers increasingly dependent on realworld evidence (RWE) in optimizing spending and improving patient outcomes (page 50). And, globally, we see growing use of HTAs, bringing fresh challenges for outcomes research (page 30) but also opportunities to leverage a growing body of RWE (page 42).

The expanding utility of RWE across the spectrum of healthcare delivery continues to drive new levels of achievement in the methodologies and technologies that allow its more sophisticated analysis and management. At the same time, there is growing recognition that commonality in measurements and increased collaboration will be key to achieving their potential.

With a global team of more than 200 experts in HEOR and RWE, IMS is committed to investing in advanced informatics and technical platforms to increase the coverage, depth and integrity of available data - through partnerships and proprietary tools that leverage seamless integration of multiple datasets (page 4 & 5), new patient cohorts that bring deeper insights in support of market access (page 3) and enhancements that maintain our core data assets at the forefront of decision support (page 37). Our recent expansion of IMS LifeLink PharMetrics PlusTM has created the largest, most diverse integrated US claims database, informing the complete patient journey, with tremendous new potential for outcomes research (page 6).

As we look through the lens of 2013 to a future of ever greater challenges for healthcare, we see a marketplace moving closer to greater consensus and partnership - a marketplace that is increasingly empowered to realize opportunities for advancing health through efficiency and quality improvements.

2

HTA task force optimizes data

3

Diabetes Cohort supports access in France

4

IMS/CPRD target critical linkage in England

5

Largest US dataset expands outcomes research

INSIGHTS 6

REAL-WORLD EVIDENCE Sizing up IMS PharMetrics Plus™

11 EVIDENCE NEEDS IN ONCOLOGY Demanding new approaches 16 QUALITY MEASUREMENT Improving patient outcomes 21 OBSERVATIONAL METHODOLOGY Determining correct sample size 26 COST-EFFECTIVENESS MODELING IN CANCER Enhancing accuracy and appeal 30 MODEL ADAPTATIONS IN ASIA Ensuring credibility and relevance 37 IMS CORE DIABETES MODEL Driving new HTA insights 42 IMS SYMPOSIUM: HTA STRATEGY Learning from past submissions 50 HEALTHCARE SPENDING Savings from responsible drug use PROJECT FOCUS 58 ORPHAN DISEASE Achieving market access in Europe 60 GLAUCOMA Demonstrating cost-effectiveness in Asia IMS HEOR & RWE SOLUTIONS OVERVIEW 62 ENABLING YOUR REAL-WORLD SUCCESS Solutions, locations and expertise

I hope you will enjoy this issue of ACCESSPOINT. AccessPoint is published twice yearly by the IMS HEOR and Real-World Evidence Solutions team.

VOLUME 3 ISSUE 5. PUBLISHED NOVEMBER 2012.

Jon Resnick VICE PRESIDENT, HEOR & REAL-WORLD EVIDENCE SOLUTIONS, IMS HEALTH Jresnick@imshealth.com

ACCESSPOINT • VOLUME 3 ISSUE 5

IMS HEALTH 210 Pentonville Road, London N1 9JY, UK Tel: +44 (0) 20 3075 4800 • www.imshealth.com/heor HEORinfo@imshealth.com

©2012 IMS Health Incorporated and its affiliates. All rights reserved.

Trademarks are registered in the United States and in various other countries.

PAGE 1

NEWS | HTA TASK FORCE ENCePP/EUnetHTA collaboration marks a major step forward in optimizing the use of relevant data for effectiveness assessments – and an exciting appointment for IMS.

HTA task force leads guideline development to optimize real-world data A recent collaboration between two of Europe’s leading scientific networks - the European Network of Centres for Pharmacoepidemiology and Pharmacovigilance (ENCePP) and the European network for Health Technology Assessment (EUnetHTA) - brings a new role for IMS Health in the region’s evolving drug safety and HTA environment. The collaboration is targeting the improved availability and best use of data relevant to effectiveness assessment.

The ENCePP is coordinated by the European Medicines Agency (EMA) and developed in collaboration with European experts in the fields of pharmacoepidemiology and pharmacovigilance.

The goal of the network is to further strengthen the post-authorization monitoring of medicinal products in Europe by facilitating the conduct of multi-center, independent, postauthorization studies focusing on safety and benefit:risk, using available expertise and research experience across the region.

The EUnetHTA focuses on HTA in Europe to facilitate efficient use of resources available for HTA, to create a sustainable system of HTA knowledge sharing and to promote good practice in HTA methods and processes.

IMS partners with the ENCePP in the development and dissemination of methodological and ethical standards for conducting pharmacoepidemiological studies and pharmacovigilance evaluations, reflecting its continued dedication to excellence in research. IMS LifeLink patientlevel databases are part of the ENCePP Database of Research Resources, an electronic index of EU research organizations and available data sources in pharmacoepidemiology and pharmacovigilance. The company adheres to the ENCePP Guide on Methodological Standards and Code of Conduct on promoting scientific independence and transparency.

Through the recent establishment of an HTA task force, the ENCePP and EUnetHTA aim to lead the development of methodological guidelines on comparative effectiveness studies. As part of this task force, IMS has reaffirmed its commitment to advancing healthcare as an active scientific stakeholder in the safety/HTA arena.

For further information on ENCePP visit: Organization: http://www.encepp.eu/ Methodological standards: http://www.encepp.eu/standards_and_guidances/index.html Code of conduct: http://www.encepp.eu/code_of_conduct/index.html Database listing: http://www.encepp.eu/encepp/resourcesDatabase.jsp

Visit us online IMS HEOR and Real-World Evidence Solutions has a new, enhanced website with improved access to information, including links to our online bibliography and previous issues of AccessPoint.

Please visit us at www.imshealth.com/heor

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IMS HEOR & REAL-WORLD EVIDENCE SOLUTIONS

IMS DIABETES COHORT | NEWS Drug reform has brought new stipulations for monitoring real-world use in France, especially in diabetes. An innovative IMS patient database facilitates fulfillment of key requirements for more sustainable market access.

New Diabetes Cohort drives RWE for effective market access in France IMS recently announced a new enhancement to its existing LifeLink Electronic Medical Records (EMR) offering in France with the launch of IMS LifeLink Diabetes Cohort. The move comes in response to the increasing demand for real-world evidence in the country, following drug reform triggered by the high-profile withdrawal of a number of anti-diabetes drugs. The provision of safety and appropriate-use data is now compulsory in France to ensure continued market access, particularly for diabetes drugs.

FUTURE DEVELOPMENTS IMS continues to invest in enhancing our patient-level data assets in Europe to meet the growing demand for real-world evidence (RWE) and establish prerequisites for developing valid and reliable databases.

For further information on the insights enabled by IMS Diabetes Cohort and the way in which it can help to fulfil RWE requirements in France, please email Nathalie Grandfils at Ngrandfils@fr.imshealth.com FIGURE 1: COMPARISON BETWEEN ENTRED STUDY AND IMS DIABETES COHORT (DIABETES TYPE 2 PATIENTS)

RICH RESOURCE Diabetes Cohort is a comprehensive and clinically rich resource on diabetes patients and treatment strategies in France. By collecting detailed information from a representative sample of patients with diabetes type 1 and type 2 and combining electronic case-report forms with EMRs for all medical consultations, the dataset can help manufacturers fulfil key requirements for post-registration studies in this country. The cohort includes detail on patient profiles, therapeutic regimen, treatment duration, persistence and compliance, reasons for treatment changes, and patient journey through primary and secondary care.

FULLY VALIDATED AND GROWING Based on the input of more than 200 physicians who regularly provide longitudinal information on more than 5,500 diabetes patients, Diabetes Cohort continues to grow. Validation against the Echantillon National Témoin Représentatif des personnes Diabétiques (ENTRED) 2007-2010 study, endorsed by the French Haute Autorité de Santé (HAS), confirms the excellence and reliability of this study (Figure 1).

Entred (2007) IMS Diabetes Cohort (2012) N=3,894 N=4,185 Age Mean < 45 years 45-54 years 55-64 years 65-74 years 75-84 years ≥ 85 years

66 years 3% 13% 28% 31% 21% 4%

66.2 years 4% 12% 29% 30% 21% 4%

Time from first diagnosis Mean 0-4 years 5-9 years 10-19 years ≥ 20 years

11 years 29% 23% 28% 19%

10 years 41% 22% 27% 10%

40% 45%

48% 55%

7.1% 6.9% 35.8% 21.1% 27.4% 15.8%

7.2% 6.9% 33.4% 21.0% 24.1% 16.5%

134/77 mmHg

133.9/76.3 mmHg

BMI ≥ 30 kg/m2 Patients not on insulin Patients on insulin HbA1c Mean Median ≤ 6.5% 6.5-7% 7-8% > 8% Blood Pressure Mean

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NEWS | PATIENT PATHWAY At a time of increasing focus on outcomes-based measures in healthcare, a new IMS partnership is set to shed light on the entire patient journey in England.

IMS/CPRD agreement heralds critical linkage of GP and hospital patient data The extension of an established relationship between IMS and GPRD – the world’s largest database of anonymized longitudinal GP medical records – is paving the way for the unique linkage of patient-level primary and secondary care data in England. This will make it possible for the first time ever to capture the full patient pathway for a large population in this country.

Since 2008, the partnership between IMS and GPRD has enabled HEOR teams around the world to access significantly increased patient populations in England, for more powerful, robust analytic studies. The agreement to continue and extend the relationship follows the recent evolution of GPRD into CPRD (Clinical Practice Research Datalink), and includes the direct linking of IMS Hospital Treatment Insights (HTI) data with CPRD.

HOSPITAL TREATMENT INSIGHTS Hospital Treatment Insights is a unique dataset launched in 2012. Integrating IMS proprietary Hospital Pharmacy Audit (HPA) information with patient-level Hospital Episode Statistics (HES) outcome data, HTI enables detailed exploration of the dynamics between drug, patient and associated outcomes within the hospital environment in England.

Today, the number of patients within the dataset has reached 1.2 million across 90 hospitals in England and it continues to grow.

The longitudinal patient data, in the form of a retrospective dataset, can be interrogated to answer critical questions around patient treatments and outcomes (Figure 1).

Hospital Treatment Insights Example patient counts Oncology: >75,000 Mental health: >150,000 Respiratory conditions: >240,000 Heart disease: >375,000 Going forward, studies based on the linkage of HTI and CPRD will enable research across the spectrum of patient care in the GP and hospital setting in England, supporting clinical innovation, stronger evidence of effectiveness and improved health outcomes and public health.

For further information on the IMS/CPRD partnership or Hospital Treatment Insights, please email Josh Hiller at Jhiller@imshealth.com

FIGURE 1: HTI PROVIDES UNDERSTANDING ACROSS THE PATIENT PATHWAY Drug dispensed

Diagnosis

Discharged

• Which drugs are used for which diagnoses, and for which patient profile?

• How big is the addressable market?

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Dosage titration

Re-admission

Discharged

Change in drug regimen

Operation

• What are the treatment pathways • What procedures are and how do they vary? associated with the patient pathways?

• How often are patients re-admitted for the original diagnoses, or side effects?

Outpatient visit

• How does the treatment regimen change over time?

• How can we start to measure patient outcomes?

IMS HEOR & REAL-WORLD EVIDENCE SOLUTIONS

DATABASE EXPANSION | NEWS Landmark IMS/HIC agreement creates industry’s largest integrated US health claims database to inform the improvement of healthcare efficiency.

Enhanced dataset dramatically expands capabilities for outcomes research In early September 2012, IMS announced an agreement with Health Intelligence Company (HIC) in the US, operating as Blue Health Intelligence, to leverage HIC’s secure healthcare claims database in IMS solutions.

Offered as part of IMS LifeLink PharMetrics Plus™, this enhanced dataset builds on the IMS health plan asset known as PharMetrics, creating the largest, most diverse, integrated US claims database available. It spans geographies, care settings and industry types to bring significant gains over existing data offerings in the market: • Insight into health plan claims for more than 150 million US patients (or 1 in 3 Americans) - almost twice the size of existing claims databases • Complete provider details for improved granularity • Access to detailed information on benefit design (eg, co-pays and coinsurance amounts) and expanded details on inpatient stays (admission type, admitting diagnosis, discharge status) • Patented HIPAA-compliant de-identification and linkage technology enabling integration with other IMS assets, including Electronic Medical Records, laboratory and hospital inpatient data, as well as unique client data (eg, from registries)

KEY STEP TO IMPROVING HEALTHCARE EFFICIENCY IMS and HIC see their agreement as an important step in providing the HEOR community with increased capability for rigorous assessment of disease outcomes. Says Swati Abbott, CEO of Blue Health Intelligence, “We are very pleased to enter this agreement with IMS and accelerate our mission to leverage healthcare informatics to identify and drive gains in efficiencies and effective solutions across the healthcare system.”

A key feature of IMS PharMetrics Plus is the expansive coverage of more than 50% of the US population, with IMS tools allowing extensibility across data assets. Says Jon Resnick, VP HEOR and Real-World Evidence Solutions at IMS, “Too often, study needs require insight that spans outside a single data source. In the past, this has required assumptions and projections that have

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questioned the integrity of the output. With IMS PharMetrics Plus and IMS LifeLink we can leverage de-identification and encryption technology to connect disparate assets in an efficient manner.”

IMS continues to invest to support the increasing market demand for RWE. As Jon Resnick states, “The growing trend for technology assessments, CER and formation of bodies such as PCORI, is dramatically increasing the need for RWE solutions such as IMS PharMetrics Plus that provide insight into the quality of healthcare at a very granular level of detail.”

IMS PharMetrics Plus Example patient counts Hypertension: >4.5 million Asthma: >1.3 million Breast cancer: >0.25 million Counts are for 2011 only and include patients with both pharmacy and medical coverage

For further information on IMS PharMetrics Plus, the most comprehensive, integrated database available in the US market, please email Jovan Willford at Jovan.willford@us.imshealth.com

An inside perspective on IMS PharMetrics Plus See our Insights feature on page 6 for an industry and academic expert view on the benefits and applications of IMS PharMetrics Plus.

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INSIGHTS | REAL-WORLD EVIDENCE The recent expansion of IMS LifeLink PharMetrics Plus™ has created the industry’s most comprehensive US claims asset (see News, page 5) with the capacity for seamless integration with multiple patient-level datasets.

To explore its practical implications for advancing outcomes research, we ask two leading experts from industry and science for their take on its potential in enabling more proactive management of real-world evidence requirements.

The interviewers Jovan Willford, MBA is Principal RWE Solutions, IMS Health Jovan.willford@us.imshealth.com

Shibani M. Pokras, MPH is Director HEOR, IMS Health Spokras@us.imshealth.com

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IMS HEOR & REAL-WORLD EVIDENCE SOLUTIONS

REAL-WORLD EVIDENCE | INSIGHTS

New connections for RWE An inside perspective on IMS LifeLink PharMetrics Plus™ How will the largest US integrated claims database make a difference to the world of pharma and science? We investigate with two key users from industry and academia.

INTERVIEW WITH DAVID LAITINEN, MPH David Laitinen is Associate Director, Global HEOR at Abbott Laboratories. He previously worked for Procter & Gamble Pharmaceuticals and Pfizer and has more than 20 years experience in HEOR and disease management. David holds an MPH in Epidemiology from the University of Minnesota School of Public Health and is currently in his 6th year at Abbott. Here, he shares his views on the importance of patient-level data and the opportunities enabled by PharMetrics Plus, in discussion with Jovan Willford, Principal RWE Solutions at IMS Health.

Jovan Willford: How do you view the importance of patient-level data, especially given the growing trend of comparative effectiveness research (CER)?

David Laitinen: This is a tough question because CER has been around for many years in some form, at least among epidemiologists and biostatisticians. However, the accessibility of the data and supporting tools available now allow other scientists to perform these types of studies. For example, websites such as SAS and SUJI provide individuals who have a propensity for research to do it themselves in a way that was not available 20 years ago.

JW: In terms of utility for your research, what do you see as challenges that can’t be met with existing data today?

DL: We have 7 or 8 data resources in-house including medical claims databases, electronic medical record (EMR) databases and survey databases. None of these provide the entirety of what we need and each has strengths and weaknesses. Claims databases, for example, lack information like family history, diet, height and weight for obesity which is increasingly important for our research. EMRs, on the other hand, don’t provide the entire health history for these patients and typically don’t include hospitalizations or cost information but they have some of the patient characteristics that we lack in the claims data.

In the case of survey data, we are asking patients at a single point in time and hoping they can remember what has happened to them, what they were prescribed, whether they were compliant and how they felt about the drug. This requires a lot of memory and recall on their part and it’s on a relatively small population. The strength of medical claims and EMRs is the power of tens of millions of patients against an average of around two thousand patients in our survey data in-house.

“We have 7 or 8 data resources in-house and none of these provide the entirety of what we need.

Each has strengths and weaknesses.” continued on next page

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INSIGHTS | REAL-WORLD EVIDENCE

“The ability to take the core claims dataset and link that back to existing IMS datasets to obtain socio-demographic information, linkage to death and mortality index, and linkages to things like oncology registries, will fill holes that we haven’t been able to fill before.”

JW: Given the challenges you’ve mentioned above, I imagine some of the planned benefits for PharMetrics Plus around the de-identification, encryption and linkages would be able to address some of these asset-specific gaps and allow you to maximize the value of each of your data sources in a more comprehensive way.

DL: Yes, I’m excited about PharMetrics Plus. The ability to take the core claims dataset and link that back to existing IMS datasets to obtain socio-demographic information, linkage to death and mortality index, and linkages to things like oncology registries, will fill holes that we haven’t been able to fill before. We have a large oncology franchise with several products in development and when I try to address some of the questions they have, it’s often difficult due to lack of staging information, for example, which we can’t get from claims or EMR data. So, linking to registries will fill a big gap.

Given what I understand about PharMetrics Plus, there is potential to dramatically streamline my data management. I have a relatively small shop with a handful of programmers and we can’t adequately cover 7 or 8 assets, particularly when we’re only using an asset once or twice a year. Anything that will allow me to have a core of data assets that will help us to become more efficient and improve the integrity of our underlying research is a gain relative to the current environment.

“Given what I understand about PharMetrics Plus, there is potential to dramatically streamline my data management. It is as close to a complete dataset as I’d want, relative to anything in the market today.”

JW: In addition to the de-identification and linkage, what are the other benefits that interest you with PharMetrics Plus? Some of the additional elements we consider unique are size, provider identification, geographic granularity and richness of benefit information.

DL: PharMetrics Plus is as close to a complete dataset as I’d want, relative to anything in the market today. Size is certainly important to an extent but I think we’ve achieved scale.You have 150 million patients in the US in PharMetrics Plus which is plenty, even for orphan products and rare diseases. Outside of OMAP and some of the government solutions, private vendors will have difficulty going much beyond this level. Thankfully, we are now at a scale where sample, size and power are no longer really an issue. •

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IMS HEOR & REAL-WORLD EVIDENCE SOLUTIONS

REAL-WORLD EVIDENCE | INSIGHTS INTERVIEW WITH ERNST BERNDT, MS, PHD Ernst Berndt is Louis E. Seley Professor in Applied Economics and Professor of Applied Economics at the MIT Sloan School of Management. He is also co-director of the Biomedical Enterprise Program and was previously director of the National Bureau of Economic Research Program on Technological Progress and Productivity Measurement. His recent research has focused on the changing dynamics of the healthcare industries. Ernst holds an MS and a PhD from the University of Wisconsin. Here he shares his views on the importance of patient-level data and the opportunities enabled by PharMetrics Plus, in discussion with Shibani Pokras, Director HEOR at IMS Health.

Shibani Pokras: How do you view the importance of patient-level data, especially given the growing trend of comparative effectiveness research (CER)?

Ernst Berndt: The use of patient-level data is very critical because we want to be able to follow patients over time to track the dynamics of treatment and re-admission and longer term effects.

Secondly, one of the things that CER is really grappling with is around different subpopulations, where treatments may be very effective for one sub-population but not for another and in order to determine that, you really need patient-level data. So, I think that both for PCORI-type purposes and more generally for CER, and cost-effectiveness research for that matter, it is really critical to have data for individuals that you can follow over time, that you can link across pharmacy benefits, medical procedures, and possible lab test results, and so forth. I think it is a very important tool that will have tremendous value in future medical and cost research and CER. SP: In terms of utility for your research, what do you see as challenges that can’t be met with existing data? What types of analyses have you tried to conduct, or have thought of conducting, but couldn’t, given the current limitations of patient-level data available today?

EB: One of the things that we’d like to do is take a look at the effects of various medicines and procedures on lab test results, be it for CD4 load factor issues or HbA1c tests for diabetes. So far, unless you work with specific hospital networks databases or provider databases, you cannot gain access to lab test data. And so, to the extent that IMS can merge the new PharMetrics Plus data with lab data, it will be a really important leg up, particularly for very significant, costly diseases like diabetes, where having lab test results is really valuable.

“For PCORI-type purposes and more generally for CER and cost-effectiveness research, it is really critical to have data for individuals that you can follow over time and link across pharmacy benefits, medical procedures, possible lab test results, and so forth.”

Having lab tests may also allow you to work backwards and ask: If I have certain test results can I then say that certain subpopulations are responding differentially? Can I use that to move towards a more personalized medicine sort of treatment - which you might be able to do once you have a humongous database.

SP: Yes, at IMS we’re really excited about the size and ability to link several datasets with PharMetrics Plus. There are lots of pockets of local lab datasets that you can work with from hospital or providers, but they are very small. IMS works with one of the largest US national providers of lab data services so we anticipate that the overlap between this and our greatly enlarged PharMetrics Plus database will be much bigger than datasets currently available today. continued on next page

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INSIGHTS | REAL-WORLD EVIDENCE What are the possibilities and opportunities with PharMetrics Plus relative to what you’re using today? We’ve talked about lab data, but are there other areas where you see it might be useful in your research?

EB: One area of research that’s been developed rather well for Medicare patients is the way in which treatments differ across geographic areas, ie, the Dartmouth Atlas project. It’s not been nearly as widely done for commercially-insured populations. Indeed, some of the preliminary findings are quite different for commercial vs. Medicare populations. We see considerably less geographic variability and I think with the PharMetrics Plus data that you have at the 3-digit zip code level and at the MSA level (Metropolitan Statistical Area) you’ll be able to look at geographic variations in care for populations other than Medicare much more carefully. I would see that as a real opportunity for important additional work on costs, outcomes and treatment utilization at the detailed geographic level.

We know now that drug utilization varies much less than does utilization of certain medical procedures like C-sections and lower back pain treatments, but we still don’t have good explanations for this variability. By linking these procedures not only with very detailed geographic data but also with provider networks and hospitals, you can explore this in much greater detail.

“With a longitudinal claims database and with some genetic testing results from cancer registries, you have a very rich possibility to look at longer-term outcomes for important diseases like breast cancer.”

One of the trends in applied economic research today is figuring out new ways to integrate different databases and link them to each other. Having access to PharMetrics Plus would really facilitate that. In the healthcare context, it’s particularly valuable because you have provider networks attempting to constrain treatment variability by developing treatment protocols and so there is much more that you can model and measure to see just how effective these attempts are to control costs.

SP: Where do you see the significantly enhanced sample size offered by PharMetrics Plus having utility in your research?

EB: I’m now getting into the economics of personalized medicine and that gets you into small sub-populations. For rather small population or orphan diseases, any sort of information on geographic diversity at all is very difficult to obtain. In personalized medicine what you’d like be able to say, for example, is “what percentage of patients on Herceptin have tested positive on a HER2 test?”, and by looking at historical retrospective claims data, “how do survival rates differ based on those HER2 tests scores?” With a longitudinal claims database, and with some genetic testing results from cancer registries, you really have a very rich possibility to look at longer term outcomes for important diseases like breast cancer.

I’m very excited about this data in the long term, particularly the geographic detail and insight into hospital stay, because that has been a bit of a black box so far. Keep me posted on the progress made with PharMetrics Plus! •

“I’m very excited about this data in the long term, particularly the geographic detail and insight into hospital stay, because that has been a bit of a black box so far.”

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IMS HEOR & REAL-WORLD EVIDENCE SOLUTIONS

EVIDENCE NEEDS IN ONCOLOGY | INSIGHTS

Oncology growth drives new evidence needs Oncology is one of the fastest growing sectors in healthcare. With its rising incidence and a significant number of new molecules in development across a range of cancer indications, ensuring access to optimal treatment is an increasing challenge.

The author Karin Berger, MBA is Principal HEOR, IMS Health Kberger@de.imshealth.com

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INSIGHTS | EVIDENCE NEEDS IN ONCOLOGY

Oncology growth drives new evidence needs The special demands on cancer treatments The current reality of healthcare suggests that payer/budget holder influence will continue to escalate, limiting physician prescribing autonomy and subsequently patient access to innovative medicines.

Oncology medications are amongst the most expensive specialty drugs available. With years of research finally bearing fruit, an influx of innovative treatments for cancer, including new molecular targeted therapies, along with developments in diagnostics, genomics and proteomics, have driven tremendous growth in this sector. As more treatment options become available and their costs continue to rise, there is increasing scrutiny of the value they bring and demand for proof of their benefits in terms of patient-relevant outcomes.

A range of access hurdles are already in use by payers/budget holders to curb physician prescribing. At their heart is the goal of improving effectiveness and value in healthcare as a way of balancing growing costs and increasingly limited resources. Equal and effective for all diagnostics and treatments (including those for rare and orphan diseases), their use has become increasingly applicable to drugs being developed for cancer. After many years away from the spotlight, new oncology treatments must now compete with technologies in other indications, in terms of the benefits they bring and the proof points required to support them. With ever higher levels of performance demanded in terms of key patient outcomes, the need for evidence of their effectiveness and cost-effectiveness, based on real-world data, is imperative.

OPTIMIZING DECISION-MAKING PROCESSES Playing a key role in the desire of healthcare systems to optimize decision-making processes are evidence-based medicine (EBM), health technology assessment (HTA) and comparative effectiveness research (CER).1,2 While separate, these are critically linked and interdependent, with HTAs evaluating literature according to evidence-based criteria and CER leveraging HTA reports for analyses.

1. Evidence-based medicine The principle article on EBM by David Sackett and colleagues3 gives the following comprehensive description: â&#x20AC;&#x153;EBM is the conscientious, explicit, and judicious use of current best evidence in making decisions about the care of individual patients. The practice of EBM means integrating individual clinical expertise with the best available external clinical evidence from systematic researchâ&#x20AC;?.

EBM has the goal of achieving better value and improving health outcomes for patients.

However, cost containment in healthcare will not be assured. On the contrary, in most cases, EBM will probably reveal a better evidence base for more expensive therapeutic options.

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IMS HEOR & REAL-WORLD EVIDENCE SOLUTIONS

EVIDENCE NEEDS IN ONCOLOGY | INSIGHTS In the EU, the use of EBM is perhaps most apparent in Germany, where benefit/effectiveness data form the basis of reimbursement decisions. Recent regulations in the country stipulate that randomized controlled trials (RCTs) provide the highest level of evidence possible.4 Outcome endpoints focus on: improved state of health (morbidity), shortening of disease duration, prolongation of life (mortality), decrease of adverse events, and improved quality of life. The new benefit dossier which is statutory for drug reimbursement and pricing discussions has a primary focus on informing therapeutic benefits compared to standard therapy or alternative therapy options.

2. Health technology assessment HTA is used to give a high-quality scientific synthesis of the available evidence regarding clinical benefits, harms, economic consequences, and ethical or social issues of a therapeutic concept or a health technology. HTAs are increasingly leveraged in markets around the world as the basis for healthcare decision making. However, the results of this synthesis can often be controversial or uncertain with respect to the availability of evidence.1 3. Comparative effectiveness research The purpose of CER is to directly compare active treatments, generate information on patients, caregivers, diagnostic and therapeutic methods during daily routine, and help consumers, caregivers and policy makers come to reasonable decisions concerning the best medical care for the individual patient.5,6 The concept of CER includes various research methods, observational research, RCTs and decision analysis to explore the evidence for diagnostic tests or therapies.5 However, clinical evidence has become closely linked to health economics. Therefore, CER has to consider both benefits and costs and estimate the remaining uncertainty.1,5

Going forward, as decision makers demand ever higher levels of performance for reimbursement and market access in oncology, the ability to generate and access RWE will be essential.

Within the context of CER, the informative value and significance of observational studies and real-world data in comparison to RCTs is the subject of ongoing debate.5,7 Proposals have been put forward to explore in a scientifically rigorous manner the huge amount of available data from healthcare service and/or payer databases before starting costly RCTs. Evaluation of routine data in health service databases has thus far been challenging due to the limitations of available datasets, particularly in terms of reported outcomes measures. However, with increasing awareness of the need for RWE this may change in the future; already, key advancements, including sophisticated database linkage and integration, are opening up new possibilities.

UNIQUE CHALLENGES OF ONCOLOGY As noted by Mulvey in 2006, oncology is more dependent on an evidence-based approach to treatment than perhaps any other medical specialty8. Yet, certainly more than most, its clinical attributes brings particular challenges for demonstrating and understanding the value of competing therapeutic interventions. The mainstays of treatment, for example, may often be effective in halting tumor progression, reducing associated cancer pain, prolonging life and sometimes even curing the disease, but not without the risk of significant side effects9 â&#x20AC;&#x201C; which vary in frequency, duration and toxicity. continued on next page

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INSIGHTS | EVIDENCE NEEDS IN ONCOLOGY Furthermore, many cancer drugs require combination with one or more other agents, within the context of intricate treatment protocols, adding to both the complexity as well as the cost of therapy.

The use of multiple lines of treatment brings further complications and uncertainty for value assessments in oncology, compounded by endemic off-label prescribing of approved drugs; in the US, more than 50% of drug use in this area is estimated to be outside approved indications10, often on the basis of unclear evidence of benefit.

Against this background is a powerful patient voice in oncology that has witnessed a groundswell of lobbying and media pressure for access to the latest medical innovations.

This particularly implies a need to learn more about patientsâ&#x20AC;&#x2122; perception of their treatment for cancer, with a special focus on quality of life - one of the most relevant patient-related outcomes in this area.

A key point too, is that many cancers are rare diseases, creating issues for proving an effect through double-blind RCTs which often demand higher patient numbers than is feasible.

Ethical reasons, also, frequently stand against the realization of an RCT in oncology.

Oncology is more dependent on an evidence-based approach to treatment than perhaps any other medical specialty.8 Yet, certainly more than most, its clinical attributes brings particular challenges for demonstrating and understanding the value of competing therapeutic interventions.

Thus, with a clinical profile that challenges best efforts to understand the relative benefits of new technologies, and an emotional attachment that increases the pressure for treatment access and coverage, there is a need for healthcare providers, manufacturers and patient groups to gallvanize action for data collection in real-life in order to generate the proof they need on the effectiveness of alternative treatment options.

A KEY ROLE FOR RWE With many targeted therapies in development, expectations for progress in cancer treatment success are high among clinicians and patients. Data that is sufficient to fulfil both medical and patient needs, with efficiency measured in terms of hard clinical endpoints, cannot feasibly be collected in oncology RCTs. Especially in this case, RWE has a key role to play in informing aspects of care, including patient demographics, clinical and patient-relevant outcomes, toxicity profiles in real-life practice, patient treatment preferences, impact of off-label usage, etc, and to identify the right patient groups and measure the benefits in these often very small patient populations. Going forward, as decision makers demand ever higher levels of performance for reimbursement and market access in oncology, the ability to generate and access RWE will be essential.

With many targeted therapies in development, expectations for progress in cancer treatment success are high among clinicians and patients. RWE has a key role to play in informing aspects of care.

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EVIDENCE NEEDS IN ONCOLOGY | INSIGHTS Achieving this goal will be contingent on: • Connecting currently fragmented information through the use of enabling technologies and methodologies which allow the linkage and integration of multiple patient-level datasets for deeper insights into patient care across the entire treatment journey.

EMRs, health plan, claims and other healthcare databases have been used increasingly in recent years as single datasets for generating RWE, but are currently not typically joined across service provision.

• Moving closer towards achieving data interoperability, at a national and ultimately international level, by leveraging alliances and technical platforms for the sharing of data.

• Increasing the focus on standardized approaches to data collection, privacy and evaluation.

• Engaging with stakeholders across the healthcare sector to facilitate the application, understanding and interpretation of RWE. • Overcoming the difficulties of data generation by engendering a spirit of open collaboration in data exchange.

While RCTs remain the cornerstone for demonstrating the efficacy of medical technologies, the key challenge for reimbursement and market access in oncology will be in developing approaches that embrace the use of data from multiple sources and reduce the uncertainty for payers and other decision makers in this sector. •

1

Walley T. Translating comparative effectiveness research into clinical practice: The UK experience. Drugs, 2012; 72: 163-70 Drummond MF, Schwartz JS, Jonsson B, Luce BR, Neumann PJ, Siebert U, et al. Key principles for the improved conduct of health technology assessments for resource allocation decisions. Int J Technol Assess Health Care, 2008; 24: 244-58; discussion 362-8 3 Sackett DL, Rosenberg WM, Gray JA, Haynes RB, Richardson WS. Evidence based medicine: What it is and what it isn't. BMJ, 1996; 312: 71-2 4 Gesetz zur Neuordnung des Arzneimittelmarktes in der gesetzlichen Krankenversicherung (Arzneimittelmarktneuordnungsgesetz - AMNOG) vom 22. December, 2010. Bundesgesetzblatt, 2010; Teil 1 Nr. 67: 2262-77 5 «Sox HC, Goodman SN. The methods of comparative effectiveness research. Annu Rev Public Health, 2012; 33: 425-45 6« Tunis SR, Benner J, McClellan M. Comparative effectiveness research: Policy context, methods development and research infrastructure. Stat Med, 2010; 29: 1963-76 7 Greenfield S, Platt R. Can observational studies approximate RCTs? Value Health, 2012; 15: 215-6 8 Mulvey TM, Preserving evidence-based oncology: We can't jeopardize clinical trials. J Oncol Pract, 2006, Sept; 2(5): 204 9 Lipscomb J, Gotay CC, Synder CF. Patient-reported outcomes in cancer: A review of recent research and policy initiatives. CA Canc J Clin, 2007; 57:278-300 10 The off-label use of drugs in oncology: A position paper by the European Society for Medical Oncology (ESMO). Annals of Oncology, 2007, 18; 2:1923-25 2

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INSIGHTS | QUALITY MEASUREMENT A concerted focus on improving health outcomes is increasing the use of pay-for-performance and other programs to raise the quality of care. Opportunities exist for industry to partner with stakeholders to develop and implement quality measures that will derive the greatest economic and clinical benefits.

The author RenĂŠe JG Arnold, PHARM D, RPH is Principal HEOR, IMS Health Renee.arnold@us.imshealth.com

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QUALITY MEASUREMENT | INSIGHTS

Enhancing stakeholder relationships with quality metrics programs Quality measurement brings new opportunities Improved health outcomes are one of the central tenets of the US Patient Protection and Affordable Health Care Act (PPACA). Health plans and insurance issuers are required to show the effects of quality reporting, effective case management, care coordination and chronic disease management on this measure. For example, PPACA requires the creation of Accountable Care Organizations (ACOs) - voluntary groupings of healthcare providers who are accountable for “quality, cost and overall care of the Medicare fee-for-service (FFS) beneficiaries assigned to it.”

An ACO will provide the bulk of beneficiaries’ primary care services, must have at least 5,000 Medicare FFS beneficiaries assigned to it and, importantly, providers may benefit financially if they meet federal quality and efficiency thresholds. Thus, pay-for-performance (P4P) and other quality improvement programs are becoming increasingly important to providers and insurers as these groups attempt to improve patient outcomes.

Many P4P programs are based on quality metrics established by groups such as the National Committee for Quality Assurance (NCQA), Centers for Medicare and Medicaid Services (CMS) and URAC (Figure 1). Health insurance plans often develop their own, customized standards, typically using these metrics, for incentive programs to financially reward clinicians who have demonstrated improved patient outcomes in disease-specific measures.

FIGURE 1:

QUALITY METRICS THAT FORM THE BASIS OF MANY P4P PROGRAMS

Group

Standards

National Committee for Quality Assurance (NCQA)

Healthcare Effectiveness Data and Information Set (HEDIS®)

Centers for Medicare and Medicaid Services (CMS)

• Physician Quality Reporting System (PQRS) • Nursing Home Value-Based Purchasing Measures • Medicare Stars Measures

URAC

Individual standards, such as Diabetes Long-Term Complications Event Rate

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INSIGHTS | QUALITY MEASUREMENT These programs include, for example, mental health antidepressant medication management (eg, percentage of members aged 18 years and older, diagnosed with a new episode of major depression and treated with antidepressant medication and who remained on an antidepressant medication for at least 6 months); pharmacologic therapy for persistent asthma (eg, percentage of patients aged 5 through 50 years with a diagnosis of persistent asthma and at least one medical encounter for asthma during the measurement year, who were prescribed long-term control medication); or for coronary artery disease (CAD) (eg, percentage of patients with a diagnosis of CAD who were prescribed oral anti-platelet therapy).

PROCESS METRICS DOMINATE The majority of the measures are typically process metrics, such as percentage of patients meeting a certain criterion (eg, percentage with diabetes mellitus who had an HbA1c test performed over a one-year period) rather than outcomes measures (eg, percentage of patients who had a HbA1c test <8mg/dl over a one-year period). This is because process metrics are often easier to measure (available as counts in medical claims data, for example) and less open to interpretation and confounding by factors that have nothing to do with program implementation designed to positively influence the outcome. For example, if a health plan develops a web-based dietary program to reduce HbA1c, there are multiple factors, both measured and unmeasured, that could influence a more beneficial outcome in one patient than another, eg, amount of exercise, family support, co-morbid conditions and plan incentives.

LEVERAGING QUALITY MEASUREMENTS Because of the desire to positively influence patient experience, as well as enactment of the healthcare reform legislation and its associated financial incentives, medical groups, ACOs, managed care organizations and Integrated Delivery Networks are very interested in improving health outcomes. Pharmaceutical companies can leverage quality metrics with these stakeholders (their customers) in a number of ways: 1. A consensus-based approach to evaluating the feasibility, reliability, validity, and usability of quality performance measures 2. Development of care management programs around this stable of quality performance measures that positively influence health outcomes 3. Establishment of shared-risk programs whereby companies that cannot help lower costs while improving health outcomes from use of their drugs, will subsequently reduce the cost of these agents and reimburse the health plan for the lack of improved outcomes.

Each of these methods establishes pathways for companies to partner with their customers to help them in evaluating, measuring and implementing programs that will reward these stakeholders under the terms of P4P environments.

1. Consensus-based approach: For this method, it is essential to use existing data to establish a baseline of quality measures to be further explored, based on relevant therapeutic areas that reflect a companyâ&#x20AC;&#x2122;s likely economic value proposition. In other words, a retrospective analysis should be used to establish quality measure benchmarks that will support customer (eg, health plan) targeting and segmentation and also identify areas of greatest opportunity for the company with their customers. Ultimately, such a program can serve as the basis for an ongoing partnership and collaboration with customers by encouraging stakeholders to share their data with the company so that the hypotheses generated with the proxy data can be tested using advanced analytic methods. In addition to identifying appropriate quality metrics for a particular plan, such analytic methods can

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QUALITY MEASUREMENT | INSIGHTS support predictive models, which would identify customer programs that are more likely to deliver good health outcomes and allow the customer to target their programs more effectively.

2. Care management programs: This method engenders partnerships between a company and its customers through use of wellness and disease management programs developed to address specific constituencies within a health plan’s populace. Such programs can be built around the benchmarks established by the consensus-based approach. The company can act as a strategic operational partner, jointly developing the programs and methods of measuring outcomes in advance of program implementation to enable timely evaluation and modification of these programs. Indeed, the company can provide health information technology (HIT) tools that will aid their stakeholders in compiling claims and payment data of participating providers, aggregating data from electronic medical records, identifying patient or group risk and opportunity areas, and providing clinical decision-making support.

3. Shared-risk programs: Through the establishment of shared-risk programs, ACOs, for example, can benefit from a company’s vast knowledge of analytic database methods to produce the reports necessary to comply with CMS reporting mandates, and thus enabling the ACO to benefit monetarily from P4P programs. It is essential to efficiently link a customer’s multiple data sources to be able to evaluate the effectiveness of these programs from a real-world evidence (RWE) perspective. Furthermore, the company can help the ACO in establishing an appropriate incentive structure for participating providers around adherence to these quality metrics.

BASIS OF THE APPROACH IMS has partnered with pharmaceutical companies to help them understand the economic value proposition for their products and establish outcomes-based metrics for their patient populations. These can serve as a baseline for their performance and allow them to benchmark vs. their peer set. Based on our experience, we recommend the following approach: • Retrospective analysis using secondary patient-level data, such as IMS PharMetrics Plus, to build a RWE value proposition. The retrospective analysis allows the establishment of baseline quality measure benchmarks that will support customer (eg, health plan) targeting and segmentation and also identifies areas of greatest opportunity for the company with their customers.

• The establishment of an ongoing partnership and collaboration with customers by acquiring and integrating customer data for retrospective/prospective analytics. This allows the company to demonstrate the brand value proposition among the customer’s patient population and establish new contracting models. It further provides a platform for quality measure performance tracking to help the customer maximize financial incentives, while optimizing opportunities for the company.

The company can also offer programs designed to improve outcomes and measure the impact of these programs. In addition, data can also be leveraged to support predictive models which would identify customer programs that are more likely to deliver good health outcomes. These models allow the customer to target their programs more effectively.

continued on next page

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INSIGHTS | QUALITY MEASUREMENT FOUR-STEP PROCESS The specific steps involved are shown in Figure 2 and principally focus on: 1. Defining the market and metrics to be measured 2. Establishing quality benchmarks and performing simultaneous economic analysis using retrospective evaluation of real-world data around the metrics identified in Step 1 thereby allowing identification of customer segments 3. Applying segmentation from Step 2 to patient-level data to build target files and identify customers performing above and below the outcomes benchmarks 4. Building a partnership with customers and negotiating for receipt of their data to perform real-world analytics using the full customer dataset In these ways, companies can establish partnerships with their key customers, helping to identify which of their P4P programs can provide the greatest economic benefit for the customer. This is based on effective profiling of its population, and establishing programs (eg disease management) around the company’s therapeutic areas that will support improvements in the customer’s quality outcomes measurement and reporting.

While the post-reform environment asks healthcare stakeholders to share more of the quality and cost-risk, it also offers aligned interests between pharmaceutical companies, payers and providers. In this environment, patient satisfaction, treatment outcomes and total cost of care will receive greater scrutiny and attention, implying a stronger, more relevant role for the industry in medication adherence, population wellness and patient education than in previous years. •

FIGURE 2:

FOUR-STEP PROCESS TO ESTABLISH PARTNERSHIPS

Step 1 Definition

Market definition

Step 2 Development

Step 3 Targeting

Creation of quality benchmarks

Segmentation applied to patient-level data

Company builds customer partnership & negotiates for receipt of data

Economic evaluation

Identification of customers performing above/below national benchmarks

Integration of customer data for prospective RWA

Segmentation

Identification of customers with poor/strong economic outcomes

Analysis of full customer dataset

Data procurement

Metric finalization

Sample size estimation

Methodology agreement

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Step 4 Implementation

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OBSERVATIONAL METHODOLOGY | INSIGHTS

Sample size requirements in observational studies Observational studies are increasingly valid for informing the real-world use of a drug but have their own challenges in design and execution that are different from RCTs. Here we explore the critical issue of determining required sample size and the key considerations for ensuring subject numbers that will achieve the desired research goals.

The authors Núria Lara Surinach, MD, MSC is Principal HEOR, IMS Health Nlara@es.imshealth.com

Montse Roset Gamisans, BSC is Director HEOR, IMS Health Mroset@es.imshealth.com

Sonia Rojas Farreras, MSC is Statistician HEOR, IMS Health Srojas@es.imshealth.com

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INSIGHTS | OBSERVATIONAL METHODOLOGY

Sample size requirements in observational studies Considerations for the right calculation Sample size determination is defined as the mathematical process of deciding, before a study begins, how many subjects should be studied1 to ensure that the number of patients included will allow assessment of the desired objective(s).

Observational studies have certain characteristics that are quite distinct from randomized controlled trials (RCTs), most of them impacting on sample size requirements: • Address multiple & more general objectives rather than a specific hypothesis • Need a sample size that ensures generalizability of findings to the population (external validity) in order to yield reliable results • Require expected missing data & selection bias to be addressed in sample size • Perform multiple comparisons requiring larger sample size • Require more assumptions & larger sample size for subgroup analyses or modeling • Owing to the absence of previous data, sample size can be re-estimated during the study, based on interim analysis

CALCULATING REQUIRED SAMPLE SIZE To calculate the sample size for an observational study there are various concepts which need to be defined, as shown in Figure 1.

Firstly, the overall objective of the observational study will determine its design and the number of patients to be included. A key step in calculating sample size is to first ask the question “What kind of statement do we want to be able to make from the results of the study?” Depending on the answer, different sample sizes will be required. The study design will also determine the process for calculating the appropriate number of patients for inclusion.

Outcomes are the assessments that enable answers to the study questions which have been raised to address the objectives. The nature of the outcomes (continuous, categorical, one population, two populations, etc…) will influence the process to obtain the correct sample size.

The statistical analysis needs to be defined in the study protocol according to study objectives, design and outcomes. The statistical techniques to be used (paired data, multivariate model, covariance adjustment, etc) must also be taken into account when defining the required sample size.

In addition to the general concepts defined above, there are some further specific parameters to be considered in the sample size determination process, such as level of precision, level of confidence, statistical power and degree of variability: • Level of precision (LOP) corresponds to the exactitude in which the specific value or result is estimated in the study sample, providing a confidence interval. The definition of the LOP depends on the study design: in a single cohort study it will be defined as the range of the confidence interval obtained in the estimation; in a study comparing two cohorts of patients, it will be defined through the difference the investigator wishes to obtain between two study groups.

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OBSERVATIONAL METHODOLOGY | INSIGHTS FIGURE 1:

REQUIRED SAMPLE SIZE IS DEPENDENT ON SEVERAL CONCEPTS

Study objective

Statistical power

Significance/ confidence level

Study design

Sample size required

Precision

Outcomes

Statistical analysis

• Level of confidence provides information about the confidence of the estimation. It is expressed as a percentage and represents how often the true value of the outcome picks an answer that lies within the confidence interval defined by the LOP. • Statistical power corresponds to the probability of rejecting that there are statistically significant differences between two groups, when differences exist. Statistical power can be only defined when a specific test is defined, not when the need is to estimate a specific parameter (mean, proportion) with pre-defined precision. Any sample size can estimate the outcome of a study; the problem is the statistical power that this sample size owns.

• Degree of variability will define the size of the sample to be examined since it refers to the distribution of the attributes being measured in the population. The more heterogeneous a population, the larger the sample size required to obtain a given level of precision.

CONSIDERATIONS AND ADDITIONAL ADJUSTMENTS As noted earlier, many of the attributes that distinguish observational studies from RCTs directly impact requirements for sample size, specifically: the nature of the study objectives; the need to be able to generalize findings to the broader population; the issue of missing data and selection bias; the use of sub-group analysis and monitoring; and potential for interim analysis.

The impact of each of these elements is discussed in turn below.

1. General and multiple objectives The objectives of observational studies are usually quite general, ie, to estimate the burden of a specific disease, define the clinical management of a disease, assess patient-reported outcomes (PROs) in patients with a specific disease, assess the use of a particular treatment, etc.

Each general objective includes a list of specific objectives. For example, a study aimed at estimating the burden of a disease will need to assess different specific objectives, such as the clinical and therapeutic management of the disease, healthcare resource use, productivity loss, symptoms and quality of life. In this case, sample size calculation is based on one or more of these specific objectives, depending on the available information. If previous data exists related continued on next page

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INSIGHTS | OBSERVATIONAL METHODOLOGY to different study objectives, the required sample size should be considered for each aspect and then the highest one selected. This approach will assure the sample size needed to assess all the study objectives or sub-objectives.

2. Generalizability of results The generalizability of results to the broader population is related to the systematic error or bias. This occurs when the characteristics of the selected sample are different from those in the population. Bias is related to the patient selection process but not to the sample size calculation. In this case, no matter how large the sample size, the results obtained will always be biased and therefore the study will have no validity. As shown in Figure 2, a higher sample size will allow a lower variability, but not a higher generalizability.

3. Missing data & selection bias Two common characteristics of observational studies are missing data and selection bias. In the real world, data gaps appear in all observational studies and must therefore be taken into account in estimating sample size. Once the sample size has been calculated, based on the standard statistical approach, it must then be increased in order to cope with the expected percentage of missing data.

In observational studies comparing two or more groups, patients are not randomly assigned with equal probability. Therefore, the probability of assignment can be related to patient baseline characteristics, often resulting in non-comparable groups due to the absence of homogeneity. To address this, adjustments are applied in the statistical analysis. When complete adjusted analysis is applied, only those patients with available data in all variables will be used in the analysis.

The use of propensity scores (PS), applied in the statistical analysis, is a sophisticated approach to solving this issue. The technique attempts to estimate the effect of a treatment, or other interventions, by accounting for the covariates that predict receiving the treatment.2 PS attempts to reduce the bias that could be found in an estimate of treatment effect from simply comparing outcomes among patients from each group. The possibility of bias arises because the apparent difference between these two groups of patients may depend on characteristics that impact whether or not a patient received a given treatment, rather than being due to the effect of the treatment per se.

FIGURE 2:

HIGHER SAMPLE SIZE REDUCES VARIABILITY BUT DOES NOT INCREASE GENERALIZABILITY

Higher sample size will allow a lower variability...

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...but not a higher generalizability

IMS HEOR & REAL-WORLD EVIDENCE SOLUTIONS

OBSERVATIONAL METHODOLOGY | INSIGHTS In terms of sample size, the percentage of overlap in PS must be considered. The required sample size estimated using standard statistical techniques needs to represent the percentage of overlap regarding the final sample size.

In RCTs, the randomization enables unbiased estimation of treatment effects; for each covariate, randomization implies that treatment groups will be balanced on average, by the law of large numbers. Unfortunately, for observational studies, the assignment of treatments is not randomized. PS attempts to mimic randomization by creating a sample of patients who received the treatment that is comparable on all observed covariates to a sample of patients who did not receive the treatment.

4. Subgroup analysis & modeling In observational studies, it is quite common to compare different subgroups of patients according to site, country or clinical condition. Calculations of sample size need to take into account these kinds of subgroup analyses to ensure the minimum sample size necessary for such comparisons. If modeling is planned, the required sample size must be increased in order to obtain the minimum number of patients needed based on the characteristics of the dependent variable to be analyzed and the expected number of predictors.

5. Interim analysis In the clinical trial setting, where allocation to treatment groups is pre-determined, interim analysis allows achievement of the targeted power with the minimum sample of patients required and without introducing substantial inflation of the type I error rate. It has not been demonstrated whether the same general conclusions hold in observational studies, where exposure-group membership cannot be controlled by the investigator.

In observational studies, different parameters considered in sample size calculation can be reestimated at an interim stage, which helps to ensure optimal power for observational research with little inflation of the type I error associated with the final analysis.3 Interim analysis can provide better estimation of assumptions (treatment/covariance distribution, estimate propensity scores overlap, drop-out rates and missing data), appropriate assumptions, and potential for revising study design (exclude treatment groups, differential recruitment into small groups, etc).

In long-term longitudinal studies or when the initial numbers used to estimate the sample size are unknown (no bibliography found or hypotheses pre-determined) sometimes there is the need to have a sample size re-estimation. For example, plan to re-estimate the sample size after 10% and 20% of patients enrolled. In this case any adjustment in p-values is not required.

CONCLUSION Estimating sample size for observational studies requires careful consideration of several key parameters, including study objective, design, population characteristics, outcomes, level of precision and errors.Various adjustments related to the planned statistical analysis must also be taken into account to ensure a subject cohort that will meet the desired study objectives. â&#x20AC;˘

1

Last JM (Ed.). A Dictionary of Epidemiology. Oxford: Oxford University Press, 1995 Yue LQ. Statistical and regulatory issues with the application of propensity score analysis to non-randomized medical device clinical studies. J Biopharm Stat. 2007; 17(1):1-13 3 Gurka MJ, Coffey CS, Gurka KK. Internal pilots for observational studies. Biom J. 2010 Oct; 52(5):590-603 2

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INSIGHTS | COST-EFFECTIVENESS MODELING When demonstrating the value of new cancer treatments, well-designed cost-effectiveness models are paramount to achieve reimbursement. However, the trade-off between clinical accuracy and simplicity of the model to increase transparency is a slippery slope. It is no surprise that the ability to model cost-effectiveness with greater accuracy, relevance and decision-maker appeal is key in achieving reimbursement â&#x20AC;&#x201C; as the recent use of an 8-state model illustrates in a hematological cancer.

The authors Mark Lamotte, MD is Principal HEOR, IMS Health Mlamotte@be.imshealth.com

Stijn Vandekerckhove, MSC is Analyst HEOR, IMS Health Svandekerckhove@be.imshealth.com

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COST-EFFECTIVENESS MODELING | INSIGHTS

Other ways to model disease progression in cancer treatments A better cost-effectiveness model?

From a model structure perspective, cost–effectiveness analysis in cancer treatments can be quite straightforward: often times, modelers opt for a 3-health state structure to model disease progression - progression-free, progression, and death.

This is in line with the guidelines on good practice for decision analytic modeling outlined by Weinstein, et al, in 2003: “The structure of the model should be as simple as possible, while capturing underlying essentials of the disease process and interventions”.1 Indeed, a 3-state structure is relatively intuitive and can be populated based on clinical trial data (given the necessary assumptions and calculations).

However, clinicians may well disagree and find this structure unintuitive. It could be argued, for example, that disease progression and the treatment pathways are over-simplified. It was this sense that drove the underlying rationale for the model described below, aimed at achieving reimbursement for a novel treatment in a hematological cancer.

CLINICAL SETTING The cancer is characterized by a slow and abnormal build up of lymphocytes and is traditionally considered a chronic disease of the elderly. Patients with early-stage cancer have a good prognosis with a life expectancy of more than 10 years. However, for patients with more advanced or progressive disease, the overall survival is much shorter.

Clinical trials had been conducted to prove that patients with a worse prognosis could benefit from the new treatment. Up to this point, a single randomized controlled trial had been published. Over a median observation period of 35.5 months, two first-line treatments were compared. The new drug demonstrated a significantly better progression-free survival (PFS).

In real-life practice, patients who progress after first-line treatment can have a second course of the same treatment or become eligible for second and third treatment lines. This means that these additional lines of treatment will further postpone progression and as such mortality due to the disease. In other words, additional quality-adjusted life years could be gained.

The approach used aimed to provide a closer and country-specific reflection of the disease progression without introducing unnecessary complexity and assumptions.

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INSIGHTS | COST-EFFECTIVENESS MODELING GATHERING RESOURCE USE FOR COSTS IN THE MODEL The resource-use data for costs for the most used treatment alternatives was not available in the two foreseen base case countries. Additionally, it was unclear which would be the most used second- and third-line treatment alternatives in the countries of interest. Therefore, a Delphi panel with key opinion leaders was organized in each country. The purpose of this was to: • Clarify the treatment path for this cancer in both countries • Confirm that the current standard of care for first-line treatment matched the comparator used in the trial • Gather information on resource use per treatment (including those after first-line) The findings from this expert panel confirmed that the chosen first-line comparator was appropriate in both countries, but that the second-line and third-line treatments differed between them. Thus, in the model, it was necessary to use within the same structure other PFS and mortality data (as well as cost data) for the second and third lines of treatment.

AN ALTERNATIVE MODELING APPROACH Despite the need for this approach, the collected data combined with the results from the clinical trial provided sufficient information to populate a Markov model with 8 health states.

This is depicted in the bubble diagram in Figure 1.

FIGURE 1:

STRUCTURE OF THE MARKOV MODEL

PFS after treatment

First-line treatment

Second-line treatment

Death

BSC

PFS after third-line treatment

Third-line treatment

PFS after Second-line treatment

In a Markov model, the progression of patients is modeled over time by following them over the health states. In fact, the treatment pathway determined the flow of arrows shown in Figure 1. A returning arrow indicated that patients could remain in the same health state.

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COST-EFFECTIVENESS MODELING | INSIGHTS Consequently, the disease flow is modeled over lifetime as follows: 1.

2.

3.

4.

First-line treatment PFS after first line Second-line treatment PFS after second line

5.

6.

7.

8.

Third-line treatment PFS after third line Best supportive care (no active treatment) Death

In the model, patients are assigned a probability for each arrow (transition probabilities). For clarity of the diagram in Figure 1 the arrow for patients moving to the Death state from any state due to progression or natural mortality has been omitted. However, in the model itself, the Death state is accessed from all states.

In contrast to a 3-health state model, where typically one treatment line is examined, the additional states in this model had two important implications: 1. First of all, the Markov model was country-specific. In the 3-health state model, all data for events occurring after progression are drawn from the multi-country clinical trial and do not answer the local treatment pathways.

2. Secondly, quality of life during and after each treatment line was also modeled. As patients progressed to a second treatment line and PFS after second-line, the quality of life score was lower than PFS after first-line. Similarly, the quality of life of patients in PFS after third-line treatment was lower than after first- and second-line.

MORE APPEALING, ACCURATE AND APPLICABLE The approach taken in this analysis aimed to provide a more appealing model from a clinical perspective. Adherence to the natural flow of the disease was increased, while from a health economic perspective costs could be assigned to patients in more health states for a higher degree of accuracy.

From a payer perspective, this model can be appreciated for the same reasons. Additionally, the results provided lifelong projections of the health effects. This clinical information was complementary to the trial data of 35.5 months and thus useful for the decision maker.

However, this model was not the first of its kind. The concept had already been used in oncology in health technology assessments2 and various other publications.3,4 Although the model was subject to a number of limitations, due to the Delphi panel data and treatment data for second- and third-line options, these limitations are also present in a 3-state model. In this type of model, other assumptions will have to be made to project the treatment outcomes beyond the first-line treatment.

In summary, the approach used aimed to provide a closer and country-specific reflection of the disease progression without introducing unnecessary complexity and assumptions. The results of it paid off with reimbursement being granted. Future research and submissions may further validate and increase the awareness of this type of model. • 1

Weinstein MC, O’Brien B, Hornberger J, Jackson J, Johannesson M, McCabe C, Luce BR. Principles of good practice for decision analytic modeling in health-care evaluation: Report of the ISPOR task force on good research practices – Modeling studies.Value in Health, 2003;6(1):9-17 2 Takeda AL, Jones J, Loveman E, Tan SC, Clegg AJ. The clinical effectiveness and cost-effectiveness of gemcitabine for metastatic breast cancer: A systematic review and economic evaluation. Health Technol Assess, 2007;11(19) 3 Campbell HE, Epstein D, Bloomfield D, Griffin S, Manca A,Yarnold J, Bliss J, Johnson L, Earl H, Poole C, Hiller L, Dunn J, Hopwood P, Barrett-Lee P, Ellis P, Cameron D, Harris AL, Gray AM, Sculpher MJ. The cost-effectiveness of adjuvant chemotherapy for early breast cancer: A comparison of no chemotherapy and first, second, and third generation regimens for patients with differing prognoses. Eur J Cancer, 2011 Nov;47(17):2517-30 4 Lux MP, Hartmann M, Jackisch C, Raab G, Schneeweiss A, Possinger K, Oyee J, Harbeck N. Cost-utility analysis for advanced breast cancer therapy in Germany: Results of the fulvestrant sequencing model. Breast Cancer Res Treat, 2009 Sep;117(2):305-17

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INSIGHTS | MODEL ADAPTATIONS IN ASIA Growing emphasis on HTAs in countries across Asia Pacific is driving new challenges for HEOR in translating global models to the needs of local markets.

An IMS Workshop at the ISPOR 5th Annual Asia-Pacific Conference in Taipei drew on input from health authorities, industry and consultants in the region to consider the evolving landscape.

The author RenĂŠe JG Arnold, PHARM D, RPH is Principal HEOR, IMS Health Renee.arnold@us.imshealth.com

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IMS HEOR & REAL-WORLD EVIDENCE SOLUTIONS

MODEL ADAPTATIONS IN ASIA | INSIGHTS

Adapting global health economic models to emerging markets Key considerations for Asia Pacific Although there is probably no specific differential issue for modeling in emerging versus developed markets, the primary concern is availability of information and usually having global models designed for settings with more data. When is it acceptable to use global trial data versus a requirement for local trial data? And what if there is no official willingness-to-pay threshold, but multiple implicit thresholds based on word-of-mouth, challenging direct interpretation of the results?

WHY MODEL? Taking a step back, it is helpful to first consider what we mean by health economic (HE) models. They are a visual framework used typically to establish the cost-effectiveness and/or budget impact of a new therapy.1-11 Models are also useful in helping to determine appropriate health system (eg, hospital, region, country) reimbursement levels as well as in guiding the development of therapeutic guidelines and formulary decision making for populations and individuals.

An example of a cost-effectiveness model could be where we assess whether it is cost-effective to drink coffee in the morning (Figure 1). In this model, we use the probability and cost of drinking coffee to stay awake, requiring an antacid (Gaviscon) due to gastric distress (from the caffeine), or falling asleep in a meeting, losing one’s job and needing to advertise for a new one. Although somewhat “tongue-in-cheek”, the model serves to illustrate and quantify the multiple factors associated with decision pathways.

An example of an interactive budget impact model (BIM)4,5,10 is shown in Figure 2 for the drug argatroban in heparin-induced thrombocytopenia (HIT). The model allows end users to change assumptions, including probabilities and costs, to determine the impact on therapeutic outcomes, eg, comparative cost or cost-effectiveness of two or more pathways.

The primary concern for modeling in emerging markets is availability of information and usually having global models designed for settings with more data.

continued on next page

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INSIGHTS | MODEL ADAPTATIONS IN ASIA FIGURE 1:

DECISION-ANALYTIC MODEL FOR COFFEE DRINKING

Success!

90%

$2

0.72

Gaviscon 10%

$2 + $3

0.08

Doze Off 20%

Lose Job 100%

$2 + $300

0.20

Stay Awake 40%

Success!

100%

$0

0.40

Doze Off in Meeting 60%

Lose Job 100%

$300

0.60

Stay Awake 80% Yes

Morning Coffee?

No

FIGURE 2:

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SCREENSHOT FROM BUDGET IMPACT MODEL FOR ARGATROBAN

IMS HEOR & REAL-WORLD EVIDENCE SOLUTIONS

MODEL ADAPTATIONS IN ASIA | INSIGHTS What is lacking in a number of emerging markets, not just those in Asia Pacific, is data.

And, without the right data inputs, we have no chance of generating credible numbers.

So, how can we fill in the gaps? Although none is ideal, there are multiple ways to approach this, including: • Use of global data • Adaptation of global data to local circumstances • Collection of local data, either pre-existing or de novo

HTA REQUIREMENTS IN ASIA PACIFIC A number of countries have developed health technology assessment (HTA) guidelines that may require use of HE models (Figure 3). However, these guidelines have evolved with varying levels of complexity and in different formats.

FIGURE 3:

FORMAL REQUIREMENT FOR ECONOMIC EVALUATION OF NEW TECHNOLOGIES AROUND THE WORLD (AS OF 2012)

S. Africa, 2004

Netherlands, 1998 Australia,1993

Brazil, 2004

Italy, 1998 New Zealand, 1993 Portugal, 1999

S. Korea, 2007 Poland, 2003

BC, Canada 1995

UK, 1999

Malaysia, 2013

Taiwan, 2008 On, Canada, 1996

Norway, 2000 Thailand, 2009

Denmark, 1997 US, 1994

Sweden, 2002 France, 1997

1993

1995

1997

1999

2001

2003

2005

2007

2009

2011

2013

For example, in South Korea, Taiwan, Thailand and China, HTA is merged with policy development. Although recommended, HTA assessment is not compulsory for formulary inclusion and reimbursement decision processes. Guideline development took place in three stages: development of HTA units (1994), promotion of HTA (1994) and merging HTA with policy development (1999). The responsible organization is the Ministry of Health and HTAs involve mainly new, high-technology products. Agents are assessed on safety, efficacy, effectiveness, cost-effectiveness and social impact, and local data is usually preferred.

Diversity in evaluation requirements exists among the few Asian jurisdictions that have established HTA processes (Figure 4). Although their respective approaches to implementing HTA are different, a single top-down method or combination seems to be the norm. Lack of relevant expertise often restricts capacity and capability development. continued on next page

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INSIGHTS | MODEL ADAPTATIONS IN ASIA FIGURE 4:

COMPARISON OF HTA GUIDELINES

Key Features

UK

China

Taiwan

South Korea

Thailand

Malaysia

Type of guideline

Submission guidelines

PE guidelines

PE guidelines

PE guidelines

Submission guidelines

PE Guidelines

Year

2008

2011

2006

2006

2008

2012

Purpose of the document

Methods of HTA & appraisal

Economic analysis for pricing & reimbursement

Guide to conduct, evaluate & report PE study

Not stated

For economic evaluation of health technology

Guidelines for PE analysis

ICER

Yes

Yes

Yes

Not stated

Yes

Yes

Total C/E

Yes

Yes

Yes

Not stated

Yes

Yes

Threshold

GBP 20,000 - 30,000

None but WHO threshold noted

None

<GDP per capita

<3xGDP None per capita (THB 300,000)

CHALLENGES OF HE MODELING IN CHINA In China, the sorts of questions that health policy makers typically raise in assessing an innovative health technology include: • Were there Chinese patients in the international multi-center clinical trial as reported in the literature?

• What is the efficacy and effectiveness of the drug in the clinical trial (Phase II or III) in China or in the Chinese population in Hong Kong, Singapore, Taiwan?

• Has a systematic review of the Chinese literature been completed?

• Has the drug been approved by FDA or NICE? Although HE modeling has been widely applied internationally in the evaluation of pharmaceuticals, it is seldom used in China due to lack of both internal and external country resources. This includes lack of data, internal trained staff, capable contract research organizations (CROs) and trained academicians able to carry out the necessary evaluations.

Nevertheless, modeling analysis is mentioned in the 2011 version of the China Guidelines for Pharmacoeconomic Evaluations (Figure 5).

Policy makers and government agencies face challenges when outcomes data are adapted from the literature and cost data are collected from the local setting. Cost data can be collected in China from: clinical trials; medical insurance claims data; case records in hospitals; health statistics and information data from health authorities (30 diseases); and field study and household health surveys (every five years).

An example of an analysis completed in China to estimate factors that contribute to the effectiveness of pneumococcal vaccines (PCVs) and the impact of Prevenar in the country is a model to inform treatment patterns and understand the burden of pneumonia and meningitis

PAGE 34

IMS HEOR & REAL-WORLD EVIDENCE SOLUTIONS

MODEL ADAPTATIONS IN ASIA | INSIGHTS FIGURE 5:

CHINA GUIDELINES FOR PHARMACOECONOMIC EVALUATIONS 2011

Guideline 6: Modeling Analysis Introduction 6.1

Decision Tree Models

6.1.1 Model hypothesis 6.1.2 Model structure 6.1.3 Source of parameters 6.2

Econometric models

6.2.1 Total cost estimation & impact factor analysis 6.2.2 ICER estimation 6.2.3 Net benefit framework

caused by streptococcus pneumoniae. This analysis used a systematic literature review as the primary data source, supplemented by local collection of epidemiological data.

Although a field study might have been preferable to inform the model, there are many hurdles to this approach in China. These include: the low diagnostic rate of invasive pneumococcal disease (IPD); overuse or misuse of antibiotics leading to low isolate rates; lack of microbiological laboratory facilities to perform serotyping; lack of a surveillance system for streptococcus pneumoniae infection; the fact that PCV belongs to second category vaccines in China (thereby requiring out-of-pocket payment); and PCV having a very low coverage rate.

RECOMMENDATIONS FOR COUNTRY ADAPTATION In adapting a global HE model to a particular country it is important to consider the policy environment, whether the model will meet local requirements, and whether it will reflect local drug price and direct/indirect cost, clinical practice, disease progression, mortality and resource utilization data. It is essential to use evidence-based principles for data inputs (avoiding reliance solely on expert opinion for cost data) as well as utilities from other, representative countries if local data are not available. Obstacles that exist for adapting models from global to local include: • Lack of solid data, especially local utility data and cost data (real hospitalization cost and reimbursement cost) • Absence of information about local treatment patterns Ensuring credibility and relevance What innovative solutions can assure that local adaptation will be credible and applicable? The model should be validated/accepted by local key opinion leaders (KOLs) in the local language.

Being able to model local practice, using local treatment guidelines/clinical practices, is helpful and local cost and clinical data should be employed. continued on next page

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INSIGHTS | MODEL ADAPTATIONS IN ASIA Lessons learned from experience in adapting global models to emerging markets underscore the importance of: • Creating as sophisticated a model as country level, budget and timeframe will allow, taking into consideration: • Country level: Relative complexity of relevant PE/HTA guidelines • Budget: Consider using local labor?

• Timeframe: Prepare KOLs in advance • Collecting local literature (both in English and translate from local language if not a native speaker) about practice patterns in country of interest • Retrieving and translating international disease treatment guidelines, if available • Applying local cost sources (depending on model perspective: country, region, large hospital, local KOLs) • For BIMs, using local market access (market share) figures for the baseline (often available from the sponsor) Above all, success will be contingent on developing a model that is as sophisticated as is necessary to include all relevant components and pathways, using local, credible data whenever available, but also taking into consideration the level of sophistication of the audience and KOLs. • This article draws on debate from the IMS Workshop “Issues in adapting global health economic models to emerging markets: A conversation with health authorities, pharmaceutical industry and consultants in Asia Pacific” held during the ISPOR 5th Asia-Pacific Conference in Taipei, September, 2012 to consider issues around HTA development in emerging Asia-Pacific markets and engender dialog amongst the various stakeholders in the region. Leaders: Dr Renée JG Arnold (IMS Health & Mount Sinai School of Medicine, NY), Dr KKC Lee (Monash University, Malaysia), Dr J Xuan (Pfizer, NY, Fudan University, Shanghai, & University of Florida) and Dr S Hu (Fudan University & Shanghai Bureau of Health).

• See Project Focus on page 60 for a further example of cost-effectiveness modeling

in Asia Pacific.

1

Annemans L. Budget Impact Analysis. In: Pharmacoeconomics: From theory to practice, ed. R Arnold, pp. 109-18. Boca Raton: CRC Press (Taylor & Francis Group LLC), 2010 2 Arnold R. Cost-effectiveness analyses throughout the drug development life cycle. In: Burger’s Medicinal Chemistry, Drug Discovery and Development, ed. D Abraham. Hoboken, NJ: John Wiley & Sons, 2010 3 Arnold R, Frishman W. Health economic considerations in cardiovascular drug utilization. In: Cardiovascular Pharmacotherapeutics, ed. W Frishman, D Sica. Minneapolis: Cardiotext Publishing, 2011 4 Arnold R, Kim R, Zhou Y, Tang B. Budgetary impact of heparin-induced thrombocytopenia with thrombosis and treatment with the direct thrombin inhibitor Argatroban (P401E). Presented at the ASHP 39th Midyear Clinical Meeting, Orlando, FL, 2004 5 Arnold R, Kim R, Zhou Y, Tang B. Temporal effect of argatroban administration on cost of treatment of heparin-induced thrombocytopenia. Presented at the Annual Meeting of the American College of Clinical Pharmacy, Dallas, Texas, 2004 6 Arnold R, Pettit K, DiCesare J, Canafax D, Lewis R, Kaniecki D. Application of Medicare databases to a theoretical renal transplant Markov model. Clinical Ther, 1996; 18:28 7 Arnold R, Pettit K, DiCesare J, Fastenau J, Kaniecki D. Prospective economic evaluation in a Phase IIIb renal transplant clinical trial.Value in Health, 1998; 1:36 8 Arnold RJ. Cost-effectiveness analysis: Should it be required for drug registration and beyond? Drug Discov Today, 2007; 12:960-5 9 Arnold RJ, Ekins S. Time for cooperation in health economics among the modelling community. Pharmacoeconomics, 2010; 28: 609-13 10 Arnold RJ, Kim R, Tang B. The cost-effectiveness of argatroban treatment in heparin-induced thrombocytopenia: The effect of early versus delayed treatment. Cardiol Rev, 2006; 14:7-13 11 Bree RL, Arnold RJ, Pettit KG, Kaniecki DJ, O'Haeri C, LaFrance ND, Toaff AL. Use of a decision-analytic model to support the use of a new oral US contrast agent in patients with abdominal pain. Acad Radiol, 2001; 8:234-42

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IMS HEOR & REAL-WORLD EVIDENCE SOLUTIONS

IMS CORE DIABETES MODEL | INSIGHTS

Understanding the importance of more insightful HTA submissions Proving rigor and market relevance is increasingly important in diabetes modeling. Building on its record of robust validation, the IMS CORE Diabetes Model continues to demonstrate expanded applications that underscore the importance of more insightful HTA analyses.

The authors Phil McEwan, PHD is an independent researcher with IMS HEOR, IMS Health Phil.mcewan@heor.co.uk

David Grant, MBA is Senior Principal HEOR, IMS Health Dgrant@uk.imshealth.com

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INSIGHTS | IMS CORE DIABETES MODEL

Understanding the importance of more insightful HTA submissions Opportunities for the IMS CORE Diabetes Model The relevance of the adage 'garbage in, garbage out' is well understood by disease modelers and those undertaking a health technology assessment (HTA). In HTA submission guidelines, it is common to find considerable emphasis on justifying data input values as well as the choice of modeling approach and assumptions applied to longterm projections. The latter is particularly important in models with a lifetime perspective and presents both challenges and opportunities for the IMS CORE Diabetes Model (CDM).

THE CHALLENGES Despite adherence to the principles of Occam's Razor, diabetes is a complex disease and, therefore, the CDM is a complex model. Furthermore, the choice of sub-populations to model, the product positioning, choice of comparators, choice of underlying equations and disease progression parameters all contribute additional complexity. It is thus unsurprising that, despite input values derived from systematic reviews and the presentation of extensive sensitivity analysis, uncertainty exists around the credibility of diabetes-specific HTA submissions.

To some extent, such skepticism is understandable; it is rare to find an economic evaluation of a novel diabetes drug that isn't cost-effective. Consequently, when reviewing a new technology submission that utilizes the CDM, concerns regarding the validity of the model and its clinical predictions are inevitable.

THE OPPORTUNITIES IMS understands diabetes and how to model the disease. This is particularly relevant today because the value argument associated with novel therapeutic technologies for the management of diabetes is primarily linked to their side-effect profile; specifically, the avoidance of hypoglycemia and weight gain. This is noteworthy given the preoccupation in evaluations of diabetes models with assessing their validity in terms of cardiovascular (CV) risk prediction.

The CDM has presented at all six Mount Hood Challenge meetings where comparison of CV risk prediction is a central feature. Furthermore, the recent validation work of the CDM Version 8.5 has focused on validating the CDM to contemporary outcomes trials in diabetes.

Applying UKPDS equations To be clear, CDM's predictive validity to contemporary outcomes studies remains very favorable, despite the use of the UKPDS risk and outcomes equations1,2,3 which have been subject to criticism regarding their ongoing relevance.4,5 Querying the justification for continued use of equations derived from UKPDS data to drive CV endpoints within the CDM, is both a common and relevant question.

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IMS HEOR & REAL-WORLD EVIDENCE SOLUTIONS

IMS CORE DIABETES MODEL | INSIGHTS While the UKPDS equations are subject to criticism they also have been the most studied, being externally validated in nine published articles. The choice of validating population is important as studies that use split sample, or v-fold cross-validation are likely to show superior predictive capabilities compared to equations validated to very different populations.6 More recently, published equations such as the DCS7, DARTS8 or Freemantle9 equations have demonstrated improved external validation although they have been validated only once.

Studies reporting a lack of fit, particularly those associated with the UKPDS equations, mainly report poor calibration with a tendency to overestimate risk. When interpreting the results of risk equation validation studies, it is important to distinguish between assessing the generalizability of a risk equation and the generalizability of a diabetes model that uses that equation. This is because disease models typically calibrate and adjust risk estimates. The CDM, for example, adjusts risk prediction to accommodate use of aspirin, antihypertensive and lipid-lowering therapy. This is a form of evidence-based calibration designed to acknowledge that different levels of CV risk factor management within a population will impact upon overall CV risk.

Tracking mortality As part of the recent CDM validation work, the decline in early mortality shown in UK clinical practice10 and attributed to more effective control of lipids, blood glucose and blood pressure, has been shown to be reproducible within the CDM using only the model's clinical management settings (via the percentage prescribed aspirin, statins and ACE inhibitors). This mortality validation demonstrates how time dependent risk equation calibration enables the CDM to track declining levels of mortality in male and female type 2 diabetes patients.

Matching levels of risk factor management is an important component in setting up each validation simulation and explains how the CDM is able to consistently predict both relative and absolute diabetes-specific events.

MAINTAINING VALIDITY AND EXPANDING APPLICATIONS IMS has made a substantial investment over the last year in both validating Version 8.5 of the CDM and in supporting non product-specific applications of the model. These applications have a particular relevance to HTA as they demonstrate functional relationship within the model and the importance of insightful analysis.

1. Quantifying relationships Figure 1 is taken from recent work presented at the 48th European Association for the Study of Diabetes Meeting in Berlin, quantifying the relationship between HbA1c reduction, nonsevere hypoglycemia risk reduction and quality-adjusted life expectancy (QALE). For a range of published disutilities associated with non-severe hypoglycemia events (NSHE), the graph shows that the equivalent HbA1c reduction required to generate the same QALE difference is between 0.33% and 1.84%. This is because incrementally, even relatively large HbA1c differences have only a modest impact on future CV event rates; furthermore, the compound effect of discounting these future events is substantial. Contrast this with the impact of hypoglycemia, which is therapy-specific and therefore its effects begin to accrue immediately within the model; the impact of discounting on the impact of hypoglycemia is far less pronounced. A similar finding occurs when modeling the avoidance of weight gain. continued on next page

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INSIGHTS | IMS CORE DIABETES MODEL 2. Analyzing linked input/output patient-level data Another illustration of the capabilities of CDM 8.5 is the analysis of linked input/output patient-level data (PLD) to provide insight into the inner workings of the model. For example, Figure 2 shows simulated cost-effectiveness pairs output by the CDM linked to input baseline HbA1c and stratified into groups. In this example, high baseline HbA1c is more cost-effective because simulated patients escalate to the next line of therapy sooner, thereby incurring less additional cost.

These examples illustrate how the CDM's functionality can be used to provide insight into what drives cost-effectiveness. Technology assessments that take this approach are more likely to present compelling cases that have face validity. It is those who best understand the model, and how it works, who are most likely to achieve this. â&#x20AC;˘

FIGURE 1:

ILLUSTRATING THE HBA1C REDUCTION REQUIRED TO ACHIEVE EQUAL LIFETIME QALE BENEFIT VERSUS THE AVOIDANCE OF 1 NON-SEVERE HYPOGLYCEMIA EPISODE PER YEAR FOR A RANGE OF PUBLISHED HYPOGLYCEMIA-RELATED DISUTILITIES.

FIGURE 2:

COST-EFFECTIVENESS PLANE SHOWING INCREMENTAL COSTS AND QALYS FOR A SECOND-LINE DPP4 COMPARED TO A SULPHONYLUREA STRATIFIED BY BASELINE HBA1C

2 1.8

8000

HbA1c reduction (%)

1.6 1.4

6000

1.2 1 0.8

4000

0.6 0.4 0

-0.02

-0.015

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IMS HEOR & REAL-WORLD EVIDENCE SOLUTIONS

IMS CORE DIABETES MODEL | INSIGHTS

New version of CORE Diabetes Model released Affirming a strong commitment to ensuring that the IMS CORE Diabetes Model remains the leading non-product-specific, subscription-based model of diabetes mellitus and its complications, IMS has recently introduced the new Version 8.5. The release follows a rigorous update of the model during 2012.

KEY ENHANCEMENTS While retaining the solid epidemiological data sources (eg, UK Prospective Diabetes Study) and leading-edge modeling methodologies (eg, non-parametric bootstrapping) of previous versions, the CORE Diabetes Model V8.5 includes a number of specific data updates and user-friendly enhancements: 1. The ability to share simulation results online with team users, reducing duplication of effort. Previously, it was only possible to share simulation input settings (eg, a disease management setting).

Diabetes Model-based tools. The updated HbA1c progression approach is more conservative, in terms of between-treatment differences, which may lead to greater acceptance of the model by payers.

4. A review of default clinical settings, including recent data from the US Renal Data System for renal replacement modalities and subsequent survival. The CDC WONDER online database has been queried to inform the latest non-diabetes-specific mortality rates. In all complication sub-models, post-event mortality risks are appropriately adjusted for competing risks, leading to greater overall life expectancy and more accurate cause of death attribution in the model.

5. Better granularity of BMI-associated quality of life disutilities from the CODE-2 study is enabled; this enhancement will be particularly relevant for cost-utility analyses where differences in BMI between two treatments is a key differentiator of the clinical profile.

In line with the latest updates, the CDM User Guide has been fully revised to include all the modelâ&#x20AC;&#x2122;s features up to and including V8.5. This can be downloaded as a PDF file from the CDM Help Center accessed from the main menu.

2. The display of cumulative incidence plots over time for each diabetes complication in the model. This provides a good indication of patient complication status during progression through the simulation. The new feature is displayed alongside the standard survival curve output, which is carried over from previous versions.

IMS is confident that the changes and new features of the model and its output will enhance diabetes economic modeling work. Feedback is welcomed as users become familiar with the latest update.

3. Adjustments to the default physiological parameter progression approaches (eg, HbA1c), in response to feedback from HTA groups and cross-validation with UK Prospective

For further information on the V8.5 update of the IMS CORE Diabetes Model, please email: David Grant at Dgrant@uk.imshealth.com

REFERENCES for article, pages 37-40

1

Stevens RJ, Kothari V, Adler AI, Stratton IM. United Kingdom Prospective Diabetes Study (UKPDS) Group.The UKPDS risk engine: A model for the risk of coronary heart disease in Type II diabetes (UKPDS 56). Clinical Science (Lond), 2001 Dec;101(6): 671-9 2 Kothari V, Stevens RJ, Adler AI, Stratton IM, Manley SE, Neil HA, et al. UKPDS 60: Risk of stroke in type 2 diabetes estimated by the UK Prospective Diabetes Study risk engine. Stroke, 2002 Jul;33(7): 1776-81 3 Clarke PM, Gray AM, Briggs A, Farmer AJ, Fenn P, Stevens RJ, et al. A model to estimate the lifetime health outcomes of patients with type 2 diabetes:The United Kingdom Prospective Diabetes Study (UKPDS) Outcomes Model (UKPDS no. 68). Diabetologia, 2004 Oct;47(10): 1747-59 4 Kengne AP, Patel A, Colagiuri S, Heller S, Hamet P, Marre M, et al.The Framingham and UK Prospective Diabetes Study (UKPDS) risk equations do not reliably estimate the probability of cardiovascular events in a large ethnically diverse sample of patients with diabetes:The Action in Diabetes and Vascular Disease: Preterax and Diamicron-MR Controlled Evaluation (ADVANCE) Study.

Diabetologia, 2010 May;53(5): 821-31 5 van Dieren S, Peelen LM, Nothlings U, van der Schouw YT, Rutten GEHM, Spijkerman AMW, et al. External validation of the UK Prospective Diabetes Study (UKPDS) risk engine in patients with type 2 diabetes. Diabetologia, 2011 Feb;54(2): 264-70 6 Chamnan P, Simmons RK, Sharp SJ, Griffin SJ,Wareham NJ. Cardiovascular risk assessment scores for people with diabetes: A systematic review. Diabetologia, 2009 Oct;52(10): 2001-14 7 Elley CR, Robinson E, Kenealy T, Bramley D, Drury PL. Derivation and validation of a new cardiovascular risk score for people with type 2 diabetes:The New Zealand diabetes cohort study. Diabetes Care, 2010 Jun;33(6):1347-52 8 Donnan PT, Donnelly L, New JP, Morris AD. Derivation and validation of a prediction score for major coronary heart disease events in a U.K. type 2 diabetic population. Diabetes Care, 2006 Jun;29(6): 1231-6 9 Davis WA, Knuiman MW, Davis TME. An Australian cardiovascular risk equation for type 2 diabetes:the Fremantle Diabetes Study.

Intern Med J, 2010 Apr;40(4): 286-92 10 Charlton J, Latinovic R, Gulliford MC. Explaining the decline in early mortality in men and women with type 2 diabetes: A population-based cohort study. Diabetes Care, 2008 Sep;31(9): 1761-6

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INSIGHTS | IMS SYMPOSIUM As the use of HTAs for determining market access continues to grow worldwide, an IMS Symposium at the ISPOR 17th Annual International Meeting explored the value of monitoring data on past submissions to inform and improve the development of future indications and compounds.

Analysis of recent HTA decisions in oncology offered some intriguing insights.

The authors Michael Nelson, PHARM D is Senior Principal HEOR, IMS Health Mnelson@us.imshealth.com

Won Chan Lee, PHD is Principal HEOR, IMS Health Wclee@us.imshealth.com

PAGE 42

IMS HEOR & REAL-WORLD EVIDENCE SOLUTIONS

IMS SYMPOSIUM | INSIGHTS

Learning from global trends in HTA Lessons for an optimal HEOR strategy Today, there are a growing number of health technology assessment (HTA) agencies in countries around the world, some more prominent and systematic in their approach, but all undertaking some form of HTA evaluation. Their approval and reimbursement decisions vary considerably, even when reviewing similar efficacy, safety and economic data, with a consequent variable impact on a drug’s market access profile. And the situation continues to evolve; even countries with more mature processes are still searching for the optimal HTA model.

For health economics and outcomes researchers, the diversity and heterogeneity of HTA agencies create enormous challenges, including constantly changing evidence requirements, inconsistent availability of appropriate data, variability in methodology and interpretation of that data, and ultimately demonstrating product value across disparate markets. Real-world evidence, for example, is one data element that is used increasingly in decision making but to different degrees in different countries (Figure 1).

FIGURE 1:

COUNTRIES ARE AT DIFFERENT STAGES IN ADOPTING RWE IN DECISION MAKING

Geographical examples

Label

Price Access

Use

AMNOG ruling specifies that prices are to be re-assessed post‐launch Observational data used in post‐launch decision making Drug utilization studies specified as a condition of market access Phase IV studies potentially influencing regional payer decisions Value‐based pricing consultation will potentially re-assess price post‐launch Private payer attention to PCORI, private CER, FDAMA Sec 114 No application

Limited application

Application

continued on next page

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INSIGHTS | IMS SYMPOSIUM The extent to which HTA outcomes and guidance vary across countries can be seen in the case of sunitinib for renal cell carcinoma (RCC), which received very different ratings from four independent European HTA agencies focused on cost-effectiveness. As shown in Figure 2, the rulings ranged from full (conditional) reimbursement by the TLV (Tand-och Lakemedelsformansverket) in Sweden to complete denial of access in Scotland following review by the SMC (Scottish Medicines Consortium), reflecting their very different perceptions of the drug’s “value”.

FIGURE 2:

HTA DECISIONS ON SUNITINIB VARIED WIDELY IN FOUR EUROPEAN COUNTRIES

SMC (Scotland)

NICE (UK)

Insufficient information on overall survival, and lack of robust economic argument

Benefit in overall survival vs INF-α alone only for patients who have not been treated with other products

HTA decision

Should not be used

Should only be used in patient population where effectiveness is demonstrated

Should be restricted for use when other treatments have failed

Recommended for inclusion for conditional reimbursement

Impact on market access

Access denied by Scottish health authorities

✔ Restricted to first-line use only by PCTs

✔ Restricted to patients who have failed on INF-α or ILK-2

✔✔ Fully reimbursed as label indication (conditional – to be reassessed when more data is available)

HTA rationale

✘

Relative market access:

✘ No access

✔✔ Full access

CVZ (Netherlands)

TLV (Sweden)

Benefit unclear due to High need for treatments and orphan drug, lack of experience. even though Concerns over unknown long-term side effects cost-effectiveness has a high level of uncertainty

SMC: Scottish Medicines Consortium NICE: National Institute for Health and Clinical Excellence CVZ: College Voor Zorgverzekeringen TLV: Tand-och Lakemedelsformansverket

GROWING NEED FOR A NEW UNDERSTANDING Understanding the diverse international HTA environment and the nature of individual agency decisions is thus of growing importance for the development of an optimal health economic research strategy. The challenge is being able to use the prior learnings and incorporate them as far as possible into the clinical trial program to ensure a positive early reimbursement decision from each of the respective HTA bodies. For manufacturers developing a market access strategy, this requires answers to a number of key questions: • Did the HTA agency evaluate similar products previously – either a clinical comparator or pricing comparators? If so: • Did they interpret the clinical data in the same way?

• What were the key drivers of negative/positive appraisal decisions?

• How likely is it that an appraisal by one HTA will impact appraisals by others?

• How can the learnings from previous appraisals be used strategically to maximize the likelihood of a positive reimbursement decision?

• If the new product is first-in-class, is there another disease state that could be used as a proxy to assess how HTA agencies are likely to appraise it?

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IMS HEOR & REAL-WORLD EVIDENCE SOLUTIONS

IMS SYMPOSIUM | INSIGHTS To address these issues, there is a clear need within HEOR to monitor the influence of the information being provided to HTA agencies and leverage it for the improvement of research over time. This knowledge can serve as the basis for: 1. Discussions with clinical development to determine the design of the Phase II/III program in relation to: the comparator or comparators that are likely to be faced in reimbursement decisions across multiple jurisdictions, the endpoints (primary and secondary) and effect size.

2. Discussions with commercial functions to inform decisions around pricing, launch sequencing and the access level that is likely to be achieved, for forecasting and other functional purposes.

3. Formulary and reimbursement submissions as the basis for designing economic evaluations, modeling assumptions, cross-country communication and pricing negotiations.

SYSTEMATIC APPROACH TO DATA COLLECTION The optimal approach to collecting HTA data will vary by organization size, level of sophistication and the involvement of local affiliates. Responsibility may be assigned to a global HEOR group, local operating companies or an external vendor, depending on availability of manpower, resources and budget. In terms of timing, monitoring ideally should begin prior to the design of pivotal Phase II/III studies by engaging payers or advisors at HTA agencies early in development to establish key endpoints, continually appraise them, and update as decisions are made.

The collection process should give consideration to HTA reviews on competitors but also data on the new compound itself which can serve as an important internal resource for applying key learnings across the organization’s portfolio of products. Addressing future launches and/or indications that may be planned for the same compound is also important. The ultimate goal should be a systematic process that includes: regular updates as decisions are made by HTA agencies; details of discussions leading to decisions (where available); and provision for tracking future reviews to anticipate competitor activity.

A key issue from a company perspective is to ensure that the collection of data is combined with systematic analysis – ie, understanding not only the decision that was reached but also the rationale or discussions around it – to facilitate forward planning for the type of challenges the compound might face in reimbursement.

The value of systematic collection lies in the immediate access to the data which is shared across all functional areas within the company and resonates in a way that can influence decisions at key points during R&D. Done well, such a process can enable: • Avoidance of potential pitfalls in future submissions and a better understanding of data requirements in each market • Identification of possible data gaps within the clinical trial program and other targeted HEOR projects • Understanding of the methodologies (accepted/rejected) around health economic models that should be implemented early on or at the time of submission • Development of more innovative tactics in price negotiations with the HTA/reimbursement authorities by learning from competitor successes and failures

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INSIGHTS | IMS SYMPOSIUM APPLICATION OF HTA DATA IN ONCOLOGY Oncology is one key therapy area which has attracted increasing attention from payers in recent years following major scientific breakthroughs and strong growth in sales. Based on HTA decisions, product uptake rates vary from one country to another. Analysis of submissions in six countries (France, Spain, England, Scotland, Australia and Canada) between 2004 and 2010 shows that approval rates across four different tumor types (lung, breast, RCC and hematological malignancies) averaged close to 60%.

For RCC only, the approval rate was 32% - the lowest among the four cancers. From a country perspective, France had the highest percent of HTA approvals for RCC while Australia and Canada had the highest number of rejections. This variation among countries remains true for other tumor types.

Also evident is the growing number of submissions for lung cancer in recent years. In general, positive reviews have also been on the rise, reflecting advancements in treatment for this disease. However, in breast cancer, where medical progress has been more pronounced, the market is somewhat crowded compared to other tumor types. Thus, while the number of submissions is also similarly on the rise, there is no clear trend regarding positive and negative approvals by these agencies. However, in the case of hematological malignancies, almost all submissions were approved, reflecting the unmet needs in this area.

STUDY METHODOLOGY These findings are drawn from an IMS analysis of historic HTA data collected through a combination of stakeholder interviews by in-market IMS experts, and secondary desk research across HTA agency and national reimbursement websites. This approach enabled a wide range of data to be collected, including local information, record information, molecular order, regulatory and reimbursement status, HTA decision (clinical and economic assessments), pricing, and risk-sharing/commercial agreements.

FIGURE 3:

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DATA EXTRACTION FORM - HTA DETAILS: CLINICAL ASSESSMENT SECTION

IMS HEOR & REAL-WORLD EVIDENCE SOLUTIONS

IMS SYMPOSIUM | INSIGHTS In total, more than 100 fields were captured and used to populate a searchable Excel-based tool, with hyperlinks to key documents where available. An example of a data extraction form is shown in Figure 3.

One of the main utilities of the database is the multiplicity of data points, enabling a detailed understanding of recent trends in HTA decisions and their implications for market access in different markets. Some examples of the insights it has been possible to glean from the dataset are outlined below.

1. Drivers of negative outcomes Most HTA rejections in RCC were the result of unacceptable economic analysis, specifically “unacceptable ICERs” - a label that is open to wide interpretation. More granular analysis of HTA decisions for RCC in Australia, Canada and the UK shows that most of the ICERs exceeded a $50K willingness-to-pay threshold, resulting in a negative outcome (Figure 4). However, in two cases where the ICER was also above this level (Australia, UK) sunitinib was approved for use. Why? • Australia: In 2007, the manufacturer of sunitinib approached the Australian PBAC three times for feedback. On the first occasion, there was no economic model included in the dossier so the submission was rejected outright. The second time, the cost/utility for adverse events had been omitted and progression-free survival proxied for overall survival. Finally, on the third attempt, the manufacturer was able to address all the previous requests and, through price negotiations, reduce the ICER to an acceptable level and ultimately gain access in the country.

• UK: Also in 2007, several submissions were made for sunitinib in the UK, where potential denial of access to the drug had triggered a public outcry. Through the ensuing negotiation process, NICE began to rethink its framework for determining reimbursement, particularly in relation to the issue of prolonging life for patients with few therapy options. Sunitinib was eventually approved for a subset of patients where the drug had proven survival benefits compared to immunotherapy. NICE has since been giving more consideration to equity issues and the possibility of applying some form of weighted threshold for different diseases.

FIGURE 4:

MOST HTA ICERS EXCEEDED $50K WILLINGNESS TO PAY WITH A NEGATIVE OUTCOME

Impact of manufacturer ICER* values on HTA outcomes Manufacturer estimate of cost/QALY (US dollars)

$200k

Negative HTA outcome

Positive HTA outcome**

$175k UK

$150k $125k

AUS UK UK AUS

$100k $75k

UK UK UK UKCAN

$50k

** Positive HTA decision = recommendation for full or subset of licensed indication

UK

AUS

Everolimus Sorafenib Sunitinib Temsirolimus

AUS

$25k N

ACCESSPOINT • VOLUME 3 ISSUE 5

* All values represent ICERs that were reported by manufacturers in the HTA reports

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INSIGHTS | IMS SYMPOSIUM 2. Impact of survival data A further interesting and very relevant analysis is the degree to which overall survival (OS) versus progression-free survival (PFS) has impacted HTA decisions. On the basis of the data collected, there is no discernible salient association between OS and PFS and HTA impact in this particular tumor type. However, it is of note that all trials using PFS without OS were rejected.

3. ICER differentials Historical HTA data for RCC reveals significant disparities between the ICER estimates submitted by manufacturers and those developed by the agencies themselves, due largely to different assumptions in economic models. This signals potentially protracted negotiations to adjust the numbers during a period when time is of the essence and pricing discussions would also be ongoing. The key message here is that by ensuring robust data and a reasonable price behind the ICER, there is an opportunity for manufacturers to reduce the negotiation period from approval to reimbursement/access, and by doing so, also minimize the opportunity costs of time lost during that process.

TAKE-HOME LEARNINGS What we can see from the data is that every situation is different; the lessons vary from one tumor type to another and each approach must be tailored. Knowledge of emerging comparators, products in the pipeline and other developments that will shape the future must also be drawn into the mix. However, purely based on the historic data, there are some key lessons to be learned in this area: • Robust evidence from well-designed trials: Clinical trials must be flawlessly designed and trial data used appropriately when submitting to HTA agencies, especially in terms of the patient group, line of therapy, outcome measures, and increasingly in the case of RCC, as well as in other tumor types, treatment sequencing and use in combination.

• ICERs: Price negotiations are increasingly based on ICERs. The ICER is essentially an analytical tool employed by HTA agencies for price reductions, re-negotiations and commercial arrangements. The fact than an ICER is high does not necessarily imply automatic rejection. Even if a drug is deemed not cost-effective, there are other vehicles by which manufacturers can gain access in those countries.

• Value-based pricing and risk-sharing agreements: In 2014 the UK will move into value-based pricing, signaling the need for even greater consideration of volume/price trade-offs between sub-populations, and a premium price versus broader indications and a lower price; in Italy and a number of other countries, the use of risk-sharing and commercial arrangements is growing. Future performance is thus becoming just as relevant as data from clinical trials for price and volume negotiations - and increasing the importance of RWE.Value-based pricing will be more common in future and payer management is likely to increase, despite the low budget impact of RCC. • Country implications: In Canada, the UK and Australia, few products were reimbursed and approved, especially in RCC. The access opportunity for new products in RCC will be similar to current therapies in the near future, with the greatest payer hurdles in these countries.

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IMS SYMPOSIUM | INSIGHTS • Survival endpoints: All trials using PFS without OS as an endpoint were rejected. Trial endpoints must be carefully chosen: traditionally challenging markets demand both OS and PFS; in other markets, PFS may be sufficient for access and uptake as second-line positioning. Purely basing trials on OS is lengthy, difficult and financially burdensome but these are areas that need to be considered more carefully.

• Quality of life: Although most quality of life data favored the products under assessment, most corresponding HTA outcomes were negative (apart from France). It is possible that if future products for RCC are able to avoid or reduce certain toxicities, especially Grade 3/4 adverse events that require suspension of treatment and therefore risk compromising efficacy, then those products are likely to be heralded by these agencies.

FUTURE IMPACT OF HTA ON NEW PRODUCTS HTA decisions are becoming increasingly influential in the success of new compounds as more countries establish HTAs and require formal submission, impacting not only HEOR but all functional areas involved in the drug development process, including pricing, market access and government affairs. As HTA engagements grow ever more complex, with greater emphasis on RWE, a coherent strategy is imperative to maximize the likelihood of positive reimbursement.

In order to manage the evolving trends, monitoring HTA data has a valid and growing role to play in informing HEOR and reimbursement strategies. There is a greater need not only for real-time access to HTA data in different countries but also for the intelligence behind the decisions and the way they are made so that this can be leveraged in internal decision making.

With a growing number of NMEs in the pipeline there will be more and more HTA engagements and a greater number to consider for their impact on development. At a time of immense constraints on resources, finding the most efficient and expeditious way of doing this will be key. •

This article summarizes presentations from the IMS Symposium “International HTA trends and implications for an optimal health economic research strategy”, held during the ISPOR 17th Annual International Meeting in Washington, DC, in June 2012. Chair: Michael Nelson, Pharm D, Senior Principal HEOR, IMS Health. Speakers: Kasem S. Akhras, Pharm D, Director Global HEOR and Therapeutic Area Leader CNS, Takeda Pharmaceuticals Inc, and Won Chan Lee, PhD, Principal HEOR, IMS Heath.

IMS acknowledges the support and guidance of Pfizer Inc, in the development of the HTA database described in this article.

ACCESSPOINT • VOLUME 3 ISSUE 5

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INSIGHTS | HEALTHCARE SPENDING More responsible use of medicine by health system stakeholders and stronger alignment of their capabilities, resources and activities, could avoid half a trillion dollars in annual global health spending, according to a new study from the IMS Institute for Healthcare Informatics.

This represents 8 percent of total annual health expenditures worldwide.

The authors Murray Aitken, MBA is Executive Director, IMS Institute for Healthcare Informatics Maitken@theimsinstitute.org

Lyudmila Gorokhovich, MSC is Project Leader, IMS Institute for Healthcare Informatics Lgorokhovich@uk.imshealth.com

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HEALTHCARE SPENDING | INSIGHTS

The responsible use of medicines A half trillion dollar opportunity Improving the responsible use of medicines is an urgent health policy priority. Better medicine use can improve health outcomes and alter the way health systems operate to optimize overall spending. At a time of intensified efforts to contain medicine spending and overall health system costs, it should be a critical goal for any health system leader.

• Medicines improve quality of life and survival in the context of the larger health system: Medicines have had an unprecedented positive effect on health, leading to reduced mortality and improved quality of life. They are a key component of efficient health systems, both as a means of prevention and avoidance of costlier, more severe conditions.

Unfortunately, medicines are often overused, misused or underused, leading to avoidable and costly adverse events and inferior health outcomes.

• Higher volumes of pharmaceuticals are being consumed globally: The increased availability of necessary medicines along with expanded access by patients have driven pharmaceutical volume growth in recent years. This volume growth is the primary cause of the rise in overall pharmaceutical costs across most countries. Injectable and infusion formulations have been leading volume growth over the past ten years with an 85% increase, followed by orals and other formulations, including nasals (based on IMS Midas data for 70 countries).

• Healthcare policy is often focused on managing elements of a health system in isolation: Medicine policy that focuses on access or cost containment through price reduction is often separated from the health system’s ability to ensure appropriate use of medicines. Approaches such as health technology assessments attempt to link the value of medicines in the context of the broader health system by measuring indicators such as avoidable hospitalizations. However, these assessments are in limited use worldwide.

SIX PRIMARY LEVERS CAN IMPROVE THE RESPONSIBLE USE OF MEDICINES WORLDWIDE Our research identified six levers of opportunity which any health system leader can apply to improve the responsible use of medicines and contain overall health system costs: 1. Non-adherence to medicines occurs when patients do not take their medicines appropriately or at all. This results in avoidable and costly complications which are often more expensive than medicines and worsen health outcomes. This research includes five focus disease areas: hypertension, hypercholesterolemia, asthma, diabetes type 2 and congestive heart failure.

At a time of intensified efforts to contain medicine spending and overall health system costs, improving the responsible use of medicines should be a critical goal for any health system leader.

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INSIGHTS | HEALTHCARE SPENDING 2. Untimely or delayed medicine use occurs when patients do not obtain medicines at the right time, leading to avoidable, relatively expensive complications downstream. In this research, the focus is on hepatitis B and C and diabetes type 2 as examples.

3. Antibiotic misuse/overuse occurs due to the perception by many that antibiotics can treat any kind of infection. Their availability and low price combined with this misperception drives misuse and overuse. Misuse results in downstream avoidable costs through hospitalizations and more expensive treatment.

4. Medication errors occur along the following processes: Prescribing, preparing and dispensing, administration and monitoring. Within each process, errors by stakeholders result in avoidable hospitalizations and additional medicine use.

5. Suboptimal generics use occurs if there is an unexploited opportunity to save costs due to greater use of safe, low-cost generics in the unprotected market. This opportunity depends on price and volume differences between off-patent and never protected medicines. The mix of these medicines can be adjusted to reduce health system costs, provided health outcomes are not undermined.

6. Mismanaged polypharmacy occurs when there is limited oversight by healthcare professionals of patients taking multiple medicines concurrently. The risk of costly and adverse events increases with age, particularly when patients are more than 60 years old and when patients take more than five medicines concurrently.

SECONDARY LEVERS In addition, we have identified three secondary levers of opportunity which apply more selectively in some countries: 1. Supply disruptions through medicine shortages and substandard medicines.

Medicine shortages occur when suppliers are unable to meet the demand for medicines, causing a disruption in patientsâ&#x20AC;&#x2122; ability to use the right medicines at the right time.

Substandard medicines refer to counterfeits, falsified, and/or spurious medicines that do not work appropriately in patients and/or delay quality treatment, potentially leading to adverse drug events.

2. Excessive use of expensive therapies occurs when costly medicines are provided to patients with limited or no consideration for their likelihood of improving health outcomes.

This lever is more relevant in countries with increasing access to innovative and expensive therapies in disease areas such as oncology. In this context, it is increasingly important to ensure that expensive medicines are not provided to patients who will not respond or are ineligible.

3. Medicine abuse of prescriptions occurs when legal medicines are taken by patients for a purpose different from their intended prescription use. Patients also abuse over-the-counter medicines through overdose and/or addiction. In these situations, patients are not taking the right medicines, contributing to avoidable adverse events and costs.

HALF A TRILLION DOLLARS OF ANNUAL COSTS CAN BE AVOIDED Our research estimates that approximately $500 billion in avoidable costs, or about 8% of annual total health expenditure worldwide, could have been realized in 2011 through optimized use of medicines (Figure 1).

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HEALTHCARE SPENDING | INSIGHTS FIGURE 1:

ESTIMATED AVOIDABLE COSTS FROM SUBOPTIMAL USE OF MEDICINES

BREAKDOWN OF THE HALF A TRILLION DOLLARS Use low-cost generics where available

Avoidable cost opportunity (Billions USD)

$62

$54

Improve adherence Ensure timely medicine use

$269

Optimize antibiotic use

$42 $30

TOTAL

$475

Prevent medication errors $18 Manage polypharmacy

These figures were derived from a global modeling analysis based on best available data from 186 countries. The model assumes that health outcomes will be at minimum maintained or improved. Factors considered for country variation in the analysis include demographics, noncommunicable disease risk factors, healthcare access, health system infrastructure and medicine intensity, including antibiotic use in the system and availability of new chemical entities.

With cost containment a key concern of health ministers, the primary focus of this opportunity analysis is on cost avoidance. The opportunity should be assessed in the context of optimizing overall healthcare and reallocating avoidable costs to address other healthcare needs. The estimates shown are likely to be understated since some of the analysis focused on a limited number of disease areas.

This global quantification effort is intended to trigger a meaningful discussion not on the exact figures but rather on how to assess the impact of responsible medicine use.

HEALTH SYSTEM LEADERS CAN LEVERAGE EXISTING, PRAGMATIC EVIDENCE All healthcare stakeholders in the private and public sectors can implement recommendations leveraging existing, pragmatic evidence from around the world. The IMS Institute identified more than 50 recommendations for health ministers. Most apply to a number of private and public sector stakeholders also, including payers, prescribers, dispensers, patients and the industry.

More than a third of the recommendations require a collaborative, multi-stakeholder approach.

Each recommendation has been assessed for time to impact (maximum being five years), level of health outcome improvement and initial spending level. Prioritized recommendations with the lowest time to impact, greatest health outcome improvement and lowest spending level, primarily rely on dispensers, policymakers/payers and a collaborative effort among all stakeholders.

The five key recommendations that health system leaders can implement to drive improvement are: 1. Strengthen the role of pharmacists in medicines management 2. Invest in medical audits focused on elderly patients 3. Implement mandatory reporting of antibiotic use 4. Encourage a “no blame” culture towards error reporting 5. Support targeted disease management programs for prevalent non-communicable diseases continued on next page

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INSIGHTS | HEALTHCARE SPENDING FIVE SUCCESS FACTORS TO CONSIDER PRIOR TO IMPLEMENTATION Before implementing recommendations, health system leaders must consider a combination of five success factors: 1. Policy: Regulations or laws that support improvements in medicine use downstream among prescribers, dispensers and patients 2. Collaboration: Engagement between healthcare professionals and patients in the public and private sectors 3. Education and capacity-building efforts: Education efforts directed to healthcare professionals and patients through training and/or public awareness campaigns 4. Informatics: Applied data collection or medicine use monitoring to inform decision making, including the application of information technology (IT) and non-IT based methods 5. Incentives: Alignment of incentives among healthcare professionals to drive change in clinical and/or dispensing practice Implementing policies to support the responsible use of medicines may be challenging, but is essential. Health system leaders should not let the perfect stand in the way of the good. While an overall shift to improved policies will take time, each leader can take initial, necessary steps to advance the responsible use of medicines in the near term.

RECOMMENDATIONS FOR ACTION HAVE BEEN IDENTIFIED ACROSS ALL SIX PRIMARY LEVERS Our research offers recommendations across all six levers of opportunity based on proven existing efforts with positive impact. Below are selected highlights prepared for Ministers of Health: • Improve adherence: Adopt cross-disease learnings, apply a patient-centered approach, support data collection efforts to enable targeted interventions and strengthen the role of healthcare professionals, particularly the pharmacists, to improve adherence. Apply lessons from Uganda’s efforts to improve adherence and outcomes for HIV positive patients and from Ipsos Marketing’s approach in the US that tracks attitudinal and behavioral drivers of persistence to identify targeted interventions with the best return on investment.

• Ensure timely medicine use: Support a national focus on early diagnosis, leverage economic evaluations and ensure targeted disease management programs for diseases like diabetes type 2 and hepatitis B and C to prevent delayed medicine use and costly complications. Using an economic evaluation process, Thailand included first-line and second-line treatment on the national medicines list, developed a free screening program and negotiated a 50% reduction in treatment prices with the industry.

• Optimize antibiotic use: Apply a combination of strategic and operational interventions to improve the use of antibiotics. For example, national treatment guidelines, patient education programs and public campaigns, and mandatory reporting of antibiotic use can make a difference as long as incentives are aligned. In China, a nationally led policy which disincentivized providers from over prescribing led to a 15% reduction in antibiotic consumption over a six-month period.

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HEALTHCARE SPENDING | INSIGHTS • Prevent medication errors: Leverage low and high resource-intensive options to reduce the likelihood of medication errors during the medicine provision process. However, technology-based efforts such as computerized decision support systems will be counterproductive without a “no blame” culture to error reporting. In the UK, such a culture driven by payer and provider management systems across the country has led to a year-on-year increase in the reporting of medication incidents at the national level.

• Use low-cost generics where available: Reassess competition policies as well as supplyside measures and adopt cross-country learnings to ensure that the savings opportunity from the use of safe and low-cost generics is galvanized. Each country in the world differs on their starting point to harness the opportunity for cost savings on medicines after patent expiry. Recommended actions depend on price differentials between unbranded and branded generics, as well as volume penetration of unbranded medicines.

• Manage polypharmacy: Implement medical audits targeting the elderly, strengthen the pharmacists’ role in the auditing process, and invest in risk stratification processes to identify patients at risk of mismanaged polypharmacy. The Health Alliance Plan in the US has different medicine management programs for patients taking eight to 19 medicines and for patients taking 20 or more. Over a three-year period the program led to a 16% reduction in prescription costs, a 21% reduction in medical costs, improved adherence and health goals (eg, HbA1c levels).

UNLEASHING THE POWER OF INFORMATION Today, more information on patients and healthcare provision is available than ever before.

Payers and providers are increasingly aware of the extent to which informatics and incentives can impact behavior to realize gains from health outcome and cost savings perspectives. We offer three observations to help health system leaders understand different dimensions of effective health informatics: 1. Advanced technology platforms are not always necessary. There are various low resource-intensive ways to collect information for decision making. Mobile health solutions and paper-based data collection systems can provide value where electronic prescribing systems and other technology-heavy systems are not possible due to resource constraints.

2. Health informatics capabilities will only deliver their full value when accompanied by efforts to drive data-driven behavior change among stakeholders. Many countries have created systems for reporting error data but must work to create a climate and culture conducive to error reporting and learning.

3. Privacy concerns, centralization vs. fragmentation and data interoperability are bottlenecks which can be overcome. Policies in the EU, Canada, Australia and Japan ensure that patients decide whether information collected about them can be disclosed to any third party. The FDA Sentinel Initiative in the US analyzes available data from multiple sources to track adverse events from medicines and medicine device use. To address challenges with data interoperability, the Continua Health Alliance, a membership-based, non-profit organization, establishes guidelines for combining and applying existing standards across patient-connected health products such as inhalers.

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INSIGHTS | HEALTHCARE SPENDING The feasibility of the recommendations put forward in the IMS Institute report varies by country and a Minister’s corresponding spheres of influence to drive change. Successful implementation depends on a blend of: policies to trigger improvements in medicine use; stakeholder collaboration; education of health professionals and patients; availability of health informatics for informed decision making; and alignment of incentives to optimize clinical and/or dispensing practices. •

BACKGROUND AND SCOPE In 2012, the IMS Institute for Healthcare Informatics and the World Health Organization were invited to contribute preparatory material for a Health Ministers summit hosted by the Dutch Ministry of Health, Welfare and Sport. As part of this effort, the IMS Institute developed a technical report, “The responsible use of medicines: Applying levers for change,” as a public service without funding from any public or private sector entity. To download the full report or individual chapters, please visit www.responsibleuseofmedicines.org The IMS Institute applied three guiding principles in preparing the report and in the recommendations to health ministers: • Use all available and relevant information • Quantify findings so that progress or setbacks can be measured and assessed • Draw conclusions that are pragmatic: do not let perfect be the enemy of the good The following working description of the responsible use of medicines was applied: The responsible use of medicines implies that the activities, capabilities and existing resources of health system stakeholders are aligned to ensure patients receive the right medicines at the right time, use them appropriately and benefit from them.

The key emphasis in this description is on the role of stakeholders in ensuring that medicines are used appropriately. When each stakeholder within the health system behaves responsibly given the best available information, medicine use can deliver very high value to the entire health system. This differs from rational medicine use which has an implicit or explicit cost implication.

The analysis draws from the World Health Organization (WHO), the World Bank, the Organisation for Economic Co-operation and Development (OECD) and IMS data sources. Details of the methodology used, together with a complete bibliography, can be found in the full report.

The IMS Institute is committed to leveraging this research as part of a broader initiative to build on and strengthen existing healthcare efforts. It is actively seeking collaboration opportunities through research and projects with different stakeholders to advance understanding of this space. Please contact info@theimsinstitute.org for further information.

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PATIENT-LEVEL DATA | IMS

Powering a patient perspective Pharmaceutical companies worldwide rely on IMS LifeLink™ to power patient-centered decisions – from the first exploratory questions that drive clinical development, through evidence generation to tactical sales planning for mature brands.

They are recognizing significant benefits, such as: • Better global decision making through consistent insights across all brands and across the product lifecycle • Improved internal alignment with consistent patient segments defined across Research & Development and Commercial functions • Enhanced communication with healthcare payers and other stakeholders through the use of a consistent patient view and common language • Faster and more accurate views across three key dimensions: patients, payers and prescribers • Confidence working with a partner who is committed to driving new metrics for new business models IMS has made extensive investments in anonymized patient-level data in markets around the world.

Today, we capture information for more than 300 million patient lives - for unparalleled real-world treatment insights.

IMS LifeLink PharMetrics Plus™ is the largest, most diverse integrated US health plan database available. With the broadest coverage of geographies, care settings and industries, it provides data on more than 150 million US patients. Through the global IMS LifeLink program, we provide a powerful patient lens to drive focus and alignment across your business, deepening your understanding of critical patient, physician and payer dynamics. LifeLink allows you to identify the right patient segments early on, in order to gain competitive advantage in today’s complex environment.

We make a patient-centered perspective easy – by integrating patient-level intelligence into our industry leading offerings and giving you expert consultants who apply it to your key issues.

LifeLink provides the insights of primary research with the benefits of secondary: lower cost, repeatable, faster, larger sample size.

IMS LifeLink has everything you need to succeed in a patient-centered universe.

AN UNPARALLELED ARRAY OF ANONYMIZED PATIENT-LEVEL DATA WORLDWIDE CANADA

EUROPE

ASIA PACIFIC

• Longitudinal Rx

• Longitudinal Rx (Belgium, France, Germany, Italy, Netherlands, Sweden, UK)

• Oncology Analyzer (China, Japan, South Korea, Taiwan)

• Electronic Medical Records (France, Germany, Italy, UK)

• Longitudinal Rx (Japan, South Korea)

• Oncology Analyzer (France, Germany, Italy, Netherlands, Spain, Turkey, UK)

AUSTRALIA

• Drug Plan Claims (Oncology, hospital)

UNITED STATES • Longitudinal Rx • Health Plan Claims • Electronic Medical Records (Oncology, ambulatory) • PharMetrics Plus • Hospital Charge Detail Master

ACCESSPOINT • VOLUME 3 ISSUE 5

• Hospital Disease Database (Belgium)

• Longitudinal Rx

• Longitudinal Patient Database (Sweden)

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PROJECT FOCUS | ORPHAN DISEASE Comprehensive approaches, transparent methodologies and the ability to enter open dialog in support of value arguments can facilitate formulary listing in increasingly pressurized markets The author Manuela Gschwend, PHD is Senior Consultant HEOR, IMS Health Mgschwend@ch.imshealth.com

Achieving market access for a novel orphan drug in Europe At a time of growing emphasis on value demonstration and more restrictive conditions for market access in countries around the world, drugs to treat orphan diseases – defined as those with a prevalence of less than 200,000 affected individuals (US) or less than 5 per 10,000 of the population (EU) – have traditionally faced lower payer pressure than technologies in other therapeutic areas.

More recently, however, with increasing numbers of highly-priced treatments for these rare conditions being submitted for reimbursement, scrutiny of this class of drugs has risen – along with demand for stronger, real-world evidence of the clinical, economic and societal benefits they bring.

Faced with this shift in the market landscape, a small biotech company came to IMS for help with the European launch of its new orphan product which had just received EMA marketing authorization. The drug was the first medicine to be licensed for the treatment of an orphan disease which had an estimated median survival time of just a few years from diagnosis.

With a small employee base and only months to go until the product was due to reach the market, the company was in urgent need of expert support for its pricing and market access activities. In IMS they found a partner that had the skills and capabilities to cover and coordinate multiple EU mid-size markets, with extensive multidisciplinary expertise, in-market presence, up-to-date countryspecific insight, established relationships with regional and local health authorities, and a proven track record of projects delivered in orphan diseases in all the focus countries.

TARGETING FORMULARY LISTING In order to meet the goal of national formulary listing in ten European markets, the IMS team defined and followed a rigorous three-step process (Figure 1). This firstly involved detailed delineation of the PMA landscape by country to determine key stakeholders, ascertain their views and expectations in this area, and explore the feasibility of establishing innovative agreements.

Drawing on the findings of this in-depth analysis, IMS then developed a comprehensive market access plan for addressing the pricing and reimbursement requirements as they applied in each market. Leveraging their longstanding relationships with local consultants and liaison with clinical and payer key opinion leaders (KOLs), the team then completed the third and final step of preparing and submitting a compelling value dossier incorporating all the necessary documentation for each individual market.

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ORPHAN DISEASE | PROJECT FOCUS FIGURE 1: THREE-STEP PROCESS TO NATIONAL FORMULARY LISTING

PMA landscape

• Who are the main stakeholders?

• How are they organized?

• What are their needs and requirements?

• What is their general view of this kind of product, comparators?

• How is this type of drug reimbursed and funded?

• What technical data are required and how should they be presented?

• What is the best way to address and educate stakeholders and communicate information?

• What are the likely timelines?

• What potential restrictions may be imposed?

• What cost-effectiveness arguments are expected?

• What agreements are feasible?

Market access plan

• Recommendations based on the alternatives available in each country • Summary of the information required to apply for reimbursement and price in each country

Preparation and submission of dossier

• Preparation of the necessary documentation in each country • Submission to the relevant authorities and follow-up of eventual queries • Work with local independent consultants who have specific expertise • Involvement of clinical and payer KOLs

Product on national list of reimbursed drugs

OVERCOMING INITIAL PUSHBACK

REIMBURSEMENT SUCCESS

The application was not without its challenges, including methodological issues within the clinical trial program, uncertainty around the magnitude of the clinical benefit and an ICER that exceeded accepted thresholds. IMS continued to work with the relevant authorities to follow up and manage their queries. Through a process of open discussion and transparency concerning cost-effectiveness and budget impact model assumptions, they were able to overcome some initial payer pushback that was encountered in a number of markets. Their ability to bring on board clinical KOLs to explain the disease gravity and unmet need was key to facilitating the application of a different value to health gain: the importance of the (small) budgetary impact and equity considerations over cost-effectiveness alone.

Clear definition of criteria for patient eligibility, prescribing and treatment cessation, together with the development of a multinational patient registry to monitor use, safety and efficacy of the product, were further important parameters leading to a successful result. Through its work with the IMS team, the company has been able to achieve reimbursement for its orphan drug in the majority of markets at the envisaged price, with additional approvals anticipated in the near future. •

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PROJECT FOCUS | GLAUCOMA Country adaptations of economic models leveraging rigorous, best-practice methodologies are key to generating value arguments in diverse local settings The authors Laetitia Gerlier, MSC is Senior Consultant HEOR, IMS Health Lgerlier@be.imshealth.com Joe Caputo, BSC is Regional Principal HEOR, Asia Pacific, IMS Health Jcaputo@sg.imshealth.com

Modeling cost-effectiveness in glaucoma across multiple Asia-Pacific markets The increasingly global scale of product launches is driving new challenges for pharmaceutical manufacturers in generating a coherent value proposition across multiple diverse markets. More countries are formalizing an HTA process and strengthening their focus on treatment cost-effectiveness but, importantly, their requirements differ. Approaches that enable core evidence for a product to be modeled and adapted to individual healthcare settings play a key role in ensuring value messages that are locally relevant but consistent with global positioning. For one leading manufacturer of eye care products, the ability to demonstrate improved long-term outcomes leveraging rigorous, best-practice model adaptations proved critical to supporting preferential product use across ten Asia-Pacific markets.

IDENTIFYING LONG-TERM VALUE The company had recently launched a new treatment for primary open angle glaucoma (POAG) - a chronic eye disease characterized by damage to the optic nerve associated with increased intra-ocular pressure. The condition progresses through three stages of severity: ocular hypertension (OHT); early, moderate and advanced glaucoma; and blindness. Glaucoma is the second leading cause of blindness after cataract1 and is estimated to affect more than 60 million people worldwide.2 Although the loss of visual fields is irreversible, the progression of glaucoma can be slowed with medication, usually in the form of eye drops. These are typically preserved to allow multiple dosing from a single bottle. However, exposure to the commonly-used preservative benzalkonium chloride (BAK) has been linked to a higher incidence of ocular surface disease (OSD) as well as issues with post-surgical outcome in glaucoma. Marking an important step forward in treatment, the company had developed an alternative BAK-free formulation. This was expected to lower the risk of OSD, translating into fewer treatment changes, improved quality of life (QoL), better compliance and potentially reduced progression of disease.

The company was keen to complement existing data on the productâ&#x20AC;&#x2122;s clinical benefits with evidence of its health economic implications, particularly in terms of long-term cost and patient outcomes. This information would be pivotal for HTA submissions, reimbursement, and pricing negotiations during the planned launch roll-out in Europe and Asia Pacific, but would require advanced capabilities in modeling outcomes across countries with very different healthcare systems and HTA requirements. In IMS HEOR they found a coordinated, global team of in-market experts with extensive experience in developing core models and seamlessly adapting them to multiple geographic settings, supported by access to a strong network of local contacts and relevant patient-level data.

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GLAUCOMA | PROJECT FOCUS The key goal was to estimate the modeled effectiveness and cost-effectiveness of treating OHT/POAG patients with the new product compared to the original brand as well as a leading BAK-preserved competitor. A core model was developed in Europe and adapted to a cross-section of countries in Asia Pacific.

Leveraging the findings of a patient chart review which had been previously undertaken by IMS HEOR in Europe, and following a systematic literature search, IMS designed a transition-state Markov model so as to capture the effects of OSD on both the risk of treatment change and disease progression.

Country-specific data included mortality, discount rates, type and frequency of medical resources used and corresponding unit costs. Resource use and glaucoma costs from a cost-ofillness study were applied to populate the core model. Unit cost data for the country settings was collected by local IMS experts in each of the markets, with microcosting allowing for adaptation of the core model. Interviews with key opinion leaders (KOLs) were conducted in each of the focus AsiaPacific countries to account for local practice in terms of type and frequency of medical resource use.

Outcomes were measured over a 10-year horizon, enabling disease progression to be followed from OHT through to more advanced stages of glaucoma. These included: • Percentage of patients with OSD: reaching second- or third-line treatment; remaining in first-line treatment at model end; requiring eye surgery/laser • Drug cost • Total cost of glaucoma management • Final visual field defect • Quality-adjusted life years (QALYs) gained Extensive sensitivity analyses were conducted on all key parameters of the model.

SIGNIFICANT COST SAVINGS REVEALED The IMS analysis showed that despite its higher unit cost, use of the new formulation was consistently associated with lower total costs, better clinical outcomes and a modest but positive impact on QoL. Both the cost savings and clinical/ QoL improvements were directly attributable to a significantly reduced incidence of OSD (Figure 1) and higher rate of compliance and persistence observed with the product versus the comparators.

FIGURE 1: OSD DEVELOPMENT OVER TIME

70% 60%

% patients with OSD

FROM GLOBAL TO LOCAL

50.7%

50% 40%

34.4%

30% 20% BAK-free product BAK-preserved product Natural OSD

10% 0% 0

1

2

3

4

5

6

7

8

9

10

Year

Development of OSD in patients receiving the BAK-free formulation was slower than for the BAK-preserved product and similar to OSD progression expected in a normal aging population without glaucoma.

Probabilistic sensitivity analysis confirmed the significance of the clinical results when using distributions around the key model parameters. The findings paved the way for further comparative studies to more closely examine the effect of the BAK-free formulation on treatment persistence and on rate of disease progression.

IMPORTANT VALUE MESSAGES FOR KEY STAKEHOLDERS Through its work with IMS, the company gained a deep understanding of the real-world value of its new formulation in terms of long-term benefits and economic consequences within the context of disparate healthcare systems in Asia Pacific. In addition to informing market access decision making and serving as a vital tool for affiliate dialogue with local payers, the clinical and cost outcomes of the model were presented at a number of specialist international conferences. At a time of continued growth in the prevalence of glaucoma they have also enabled important value messages to physicians and KOLs, serving to increase prescriber awareness of the long-term impact of BAK on the rate of OSD in glaucoma. They continue to inform price negotiations in this rapidly expanding region and justify the presence of the product on formulary listings. •

1 Resnikoff 2 Quigley

S, Pascolini D, Etya’ale D, et al. Global data on visual impairment in the year 2002. Bulletin of the World Health Organization, 2004; 82:844-51 HA, Broman AT. The number of people with glaucoma worldwide in 2010 and 2020. Br J Ophthalmology, 2006; 90: 262-267

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IMS HEOR & RWE SOLUTIONS | OVERVIEW

Enabling your real-world success IMS offers a spectrum of world-class expertise in health economics and outcomes research (HEOR) and real-world evidence (RWE) to deliver the local excellence you need.

In a future where healthcare efficiency and quality are measured through the lens of ‘real-world’ insights, external validity demands a focus on the right data sources, scientifically credible research and actionable communication.

IMS is committed to helping you succeed: • Largest multi-disciplinary team of HEOR experts, based in 18 countries worldwide • Credible scientific voice and deep therapy area knowledge, captured in 200+ publications each year • Market leadership in developing and adapting robust economic models • Most advanced capabilities in RWE management and analysis • Proven expertise in generating and communicating RWE to advance stakeholder engagement at all levels

IMS is a leading independent provider of RWE, outcomes research, economic modeling and market access solutions, and value communication, leveraging relevant IMS proprietary and other key external, third-party data assets.

Our unique, data-agnostic market position can help you develop and support the evidence required to engage global and local healthcare stakeholders, with deep insights into product safety, efficacy, cost, value for money and affordability.

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Outcomes Research

Real-World Evidence Solutions

• Evidence generation • Late-phase studies • Patient-Reported Outcomes (PROs) • Database studies • Epidemiology • Risk management

• Data sourcing & validation • Data integration & linking • Data management & curation • Platform development • Customized analytics & reporting • Evidence planning

Health Economic Modeling • Health economic evaluations • Core models & local adaptations • Budget impact • Meta-analyses • Indirect comparisons • IMS CORE Diabetes Model

Technology • Platform engines • Data warehouse/data marts • Encryption systems & linking technology • Meta-data repository • User interface & sophisticated analytics library • Electronic data capture

Market Access

IMS LifeLink

• Value development planning • Market access strategy • Core value dossiers & local adaptations • HTA readiness • Value communication • Reimbursement submissions

Largest collection of scientifically validated, anonymized patient-level data assets: • Health plan claims • Longitudinal Rx • Electronic medical records • Hospital disease • Oncology • Diabetes

TM

IMS HEOR & REAL-WORLD EVIDENCE SOLUTIONS

LOCATIONS | IMS

Global scope, local expertise IMS HEOR and RWE experts are located in 18 countries worldwide and they have published on projects completed in more than 50 countries on all continents.

YOUR PRIMARY CONTACTS Dr. Xavier Badia Senior Principal HEOR IMS Health Doctor Ferran 25-27 08034 Barcelona Spain Tel: +34 93 749 63 00 Xbadia@es.imshealth.com

Dr. Jacco Keja Senior Principal HEOR IMS Health 210 Pentonville Road London N1 9JY United Kingdom Tel: +31 (0) 631 693 939 Jkeja@nl.imshealth.com

Adam Lloyd Senior Principal HEOR IMS Health 210 Pentonville Road London N1 9JY United Kingdom Tel: +44 (0)20 3075 4800 Alloyd@uk.imshealth.com

Dr. Michael Nelson Senior Principal HEOR IMS Health 1725 Duke Street, Suite 510 Alexandria, VA 22314 USA Tel: +1 703 837 5150 Mnelson@us.imshealth.com

IMS HEOR & RWE Solutions office locations NORTH AMERICA REGIONAL HEADQUARTERS 11 Waterview Boulevard Parsippany, NJ 07054 USA Tel: +1 973 316 4000

EUROPE REGIONAL HEADQUARTERS 210 Pentonville Road London N1 9JY United Kingdom Tel: +44 (0)20 3075 4800

UNITED STATES 1725 Duke Street Suite 510 Alexandria, VA 22314 USA Tel: +1 703 837 5150

BELGIUM Medialaan 38 1800 Vilvoorde Belgium Tel: +32 2 627 3211

One IMS Drive Plymouth Meeting PA 19462 USA Tel: +1 610 834 0800 CANADA 16720 Route Transcanadienne Kirkland, Québec H9H 5M3 Canada Tel: +1 514 428 6000 LATIN AMERICA REGIONAL HEADQUARTERS Insurgentes Sur # 2375 5th Floor, Col. Tizapan Mexico City D.F. - C.P. 01090 Mexico Tel: +52 (55) 5062 5239 or +1 917 542 5844

FRANCE 29ème Etage Tour Ariane 5-7 Place de la Pyramide 92088 La Défense Cedex France Tel: +33 1 41 35 1000 GERMANY Erika-Mann-Str. 5 80636 München Germany Tel: +49 89 457912 6400 ITALY Viale Certosa 2 20155 Milano Italy Tel: +39 02 69 78 6721

SPAIN Dr Ferran, 25-27 08034 Barcelona Spain Tel: +34 93 749 63 00 SWEDEN Sveavägen 155/Plan9 11346 Stockholm Sweden Tel: +46 8 508 842 00 SWITZERLAND Theaterstr. 4 4051 Basle Switzerland Tel: +41 61 204 5071 UNITED KINGDOM 210 Pentonville Road London N1 9JY United Kingdom Tel: +44 (0)20 3075 4800 ASIA PACIFIC REGIONAL HEADQUARTERS 10 Hoe Chiang Road Keppel Towers #23-01/02 Singapore 089315 Tel: +65 6412 7365

AUSTRALIA Level 5, Charter Grove 29-57 Christie Street St Leonards, NSW 2065 Australia Telephone: +61 2 9805 6800 CHINA 7/F Central Tower China Overseas Plaza Jianguomenwai Avenue, Chaoyang District Beijing 100001 China Tel: +86 10 8567 4255 SOUTH KOREA 9F Handok Building 735 Yeoksam1-dong Kangnam-ku Seoul 135-755 S. Korea Tel: +82 2 3459 7307 TAIWAN 8th Floor No 2, Tun Hwa South Road Section 1 Taipei 10506 Taiwan ROC Tel: +886 2 2721 5337

FOR FURTHER INFORMATION: email HEORinfo@imshealth.com or visit www.imshealth.com/heor

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IMS | EXPERTISE

Expertise in depth IMS has one of the largest global teams of experts in HEOR, RWE and market access of any organization in the world.

We apply unrivalled experience and specialist expertise to help our clients meet the demands of an increasingly complex global, regional and local pharmaceutical landscape. Our highly qualified, multi-disciplinary consultants and researchers have proven skills and capabilities across all key therapy areas. Spanning industry, consulting, government and academia, their expertise reflects a global grasp, local experience and a unique, inside market perspective.

Our senior team Renée J. G. Arnold, PHARM D • Dr. Renée Arnold is Principal HEOR at IMS Health with particular expertise in the use of technology to collect and/or model real-world data to facilitate rational decision making by healthcare practitioners and policy makers.

• Renée was previously President and CEO, Arnold Consultancy & Technology where she developed and oversaw outcomes research for the pharmaceutical, biotech and device industries as well as federal government programs.

Her distinguished career in health economics and outcomes research includes roles as President and co-founder of Pharmacon International, Center for Health Outcomes Excellence and Senior VP and Medical Director at William J Bologna International.

• Founding member and former Chair of the Education Committee of ISPOR, Renée has several adjunct appointments and is the author of numerous articles on pharmacoeconomics and cost-containment strategies.

She holds a Doctor of Pharmacy degree from the University of Southern California in Los Angeles.

Xavier Badia, MD, MPH, PHD • Dr. Xavier Badia is Senior Principal HEOR at IMS Health with extensive experience in all areas of outcomes research.

• A founder of Health Outcomes Research Europe, Xavier has extensive experience in consulting and research outcomes, patient-reported outcomes, and effectiveness and cost-effectiveness evaluations. A respected scientific speaker and member of EuroQol since 1993, he serves on several international advisory and editorial boards and has published over 150 peer-reviewed papers.

• Xavier holds an MD, a PhD in Medicine, and a Master’s in Public Health and Health Economics from the University of Barcelona.

Karin Berger, MBA • Karin Berger is Principal HEOR at IMS Health with a particular focus on real-world evidence, patient- reported outcomes, and cost-effectiveness evaluation analyses at a national and international level.

• Formerly Managing Director of MERG (Medical Economics Research Group), an independent German organization providing health economics services to the pharmaceutical industry, university hospitals and European Commission, Karin has more than 14 years experience in the health economics arena. She lectures at several universities, has published extensively in peer-reviewed journals, and regularly presents at economic and medical conferences around the world.

• Karin graduated as Diplom-Kaufmann (German MBA equivalent) from the Bayreuth University, Germany, with a special focus on health economics.

Christopher M. Blanchette, PHD, MS, MA • Dr. Chris Blanchette is Principal HEOR at IMS Health, with extensive experience in retrospective database research and observational studies with particular emphasis on respiratory diseases.

• Chris was previously Director of Clinical & Outcomes Research at the Lovelace Respiratory Research Institute, where he led a team of 50 people conducting clinical research, biostatistics, and outcomes research programs.

He also spent time in market access and R&D health economics at GlaxoSmithKline, most recently as Director for Health Data Analytics, and was formerly a researcher at the Premier Hospital Alliance and Agency for Healthcare Research and Quality.

• In addition to serving in leadership positions for ISPOR and several respiratory disease associations, Christopher is on the editorial board of the Journal of Medical Economics and advisory board of Current Medical Research and Opinion. He holds a PhD in Pharmaceutical Health Services Research and an MS in Epidemiology from the University of Maryland, and an MA in Medical Sociology from the University of North Carolina.

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IMS HEOR & REAL-WORLD EVIDENCE SOLUTIONS

EXPERTISE | IMS Nevzeta Bosnic, BA • Nevzeta Bosnic is Principal at IMS Brogan, where she manages projects to meet the broad spectrum of client needs in the Canadian pharmaceutical market.

• Formerly Director of Economic Consulting at Brogan Inc, Nev has led many strategic consulting, policy and data analyses for pharmaceutical clients, government bodies and academic institutions in Canada. She has extensive knowledge of public and private drug plans across the country and in-depth expertise and experience on the drug reimbursement process.

• Nev holds a Bachelor’s degree in Business Economics from the School of Economics and Business at the University of Sarajevo, Bosnia-Herzegovina.

Joe Caputo, BSC • Joe Caputo is Regional Principal HEOR, Asia Pacific at IMS Health leveraging more than 20 years experience in the pharmaceutical sector to help clients address the challenges of global reimbursement and market access throughout the drug development program. He has led numerous projects involving payer research, value dossiers, local market access models and HTA submissions.

• With a background that spans industry roles in drug development, sales & marketing and UK & global health outcomes, and consulting in health economics, Joe has wide-ranging knowledge of the drug development process at both local and international level and a unique understanding of evidence gaps in light of reimbursement and market access requirements.

• Joe holds a BSc in Applied Statistics and Operational Research from Sheffield Hallam University, UK.

Frank-Ulrich Fricke, PHD, MSC • Dr. Frank-Ulrich Fricke is Principal HEOR at IMS Health and Professor for Health Economics, Georg-Simon-Ohm University of Applied Sciences, Nuremberg in Germany, with a focus on health economic evaluations, market access strategies and health policy.

• Formerly a Managing Director of Fricke & Pirk GmbH, and previously Head of Health Economics at Novartis Pharmaceuticals, Frank-Ulrich has conducted health economic evaluations across a wide range of therapeutic areas, developing a wealth of experience in pricing, health affairs and health policy. As a co-founder of the NIG 21 association, he has forged strong relationships with health economists, physicians and related researchers working in the German healthcare system.

• Frank-Ulrich holds a PhD in Economics from the Bayreuth University, and an MBA equivalent from the ChristianAlbrechts-University, Kiel.

David Grant, MBA • David Grant is Senior Principal HEOR at IMS Health, specializing in reimbursement and market access, environmental analysis, prospective and retrospective data collection and communications for product support.

• A co-founder and former Director of Fourth Hurdle, David’s experience spans 10 years in health economics and outcomes research consulting, and 15 years in the pharmaceutical industry, including roles in clinical research, new product marketing and health economics in the UK and Japan.

• David holds a degree in Microbiology and an MBA from the London Business School.

Joshua Hiller, MBA • Joshua Hiller is Principal Real-World Evidence Solutions at IMS Health, supporting the strategic planning and development of IMS capabilities for data sourcing, integration, analytics and studies. He is also currently serving as Alliance Director in the IMS collaboration with AstraZeneca for the advancement of RWE. • During a career that includes roles in market analytics, government and healthcare consulting in both the US and UK, Joshua has led a wide range of projects for clients in the pharmaceutical and biotech sector as well as industry associations. He has extensive experience in pharmaceutical pricing, contracting, market landscape development, supply management, cross border trade, lifecycle management, competitive defense, generics market drivers and account management, with expertise across US and European markets.

• Joshua holds an MBA (Beta Gamma Sigma) from Columbia Business School, New York, and a BS in Mathematics from James Madison University, Virginia.

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IMS | EXPERTISE Jacco Keja, PHD • Dr. Jacco Keja is Senior Principal HEOR at IMS Health, drawing on deep expertise in global market access, operational and strategic pricing, and health economics and outcomes research.

• Jacco’s background includes four years as global head of pricing, reimbursement, health outcomes and market access consulting services at a large clinical research organization and more than 13 years experience in the pharmaceutical industry, including senior-level international and global roles in strategic marketing, pricing and reimbursement and health economics.

• Jacco holds a PhD in Biology (Neurophysiology) from Vrije Universiteit in Amsterdam, a Masters in Medical Biology, and an undergraduate degree in Biology, both from Utrecht. He is also visiting Professor at the Institute of Health Policy & Management at Erasmus University, Rotterdam.

Mark Lamotte, MD • Dr. Mark Lamotte is Principal HEOR at IMS Health with responsibility for the content and quality of all health economic evaluations conducted by his team.

• A physician by training (cardiology), Mark spent a number of years in medical practice before joining Rhône-Poulenc Rorer as Cardiovascular Medical Advisor and later becoming Scientific Director at the Belgian research organization, HEDM. He has since worked on more than 150 projects, involving expert interviews, patient record reviews, extensive modeling and report writing across a wide range of therapy areas, and authored many peer-reviewed publications.

• Mark holds an MD from the Free University of Brussels (Vrije Univeristeit Brussel, Belgium) and is fluent in Dutch, French, English and Spanish.

Won Chan Lee, PHD • Dr. Won Chan Lee is Principal HEOR at IMS Health, specializing in prospective and retrospective health economics research.

• Over the course of his career, Won has completed numerous international economic evaluations employing a variety of analytical methods across a range of diseases and geographies. His expertise includes econometric database analysis, quality of life assessment and advanced economic modeling to establish the economic and humanistic value of new and existing therapeutic interventions.

• Won holds a Master’s in Economics from the University of Grenoble II, and a PhD in Economics from the Graduate Center of the City University of New York.

Claude Le Pen, PHD • Dr. Claude Le Pen is a member of the strategic committee of IMS Health and Professor of Health Economics at Paris-Dauphine University, providing expert economic advisory services to the consulting practice.

• A renowned economist, leading academic, and respected public commentator, Claude has served as an appointed senior member of several state commissions in the French Ministry of Health and is an expert for a number of parliamentary bodies, bringing a unique perspective and unparalleled insights into the economic evaluation of pharmaceutical technologies at the highest level.

• Claude studied Business Administration in HEC Business School in Paris and holds a PhD in Economics from Panthéon-Sorbonne University.

Adam Lloyd, MPHIL, BA • Adam Lloyd is Senior Principal HEOR at IMS Health, with a particular focus on economic modeling and the global application of economic tools to support the needs of local markets.

• A former founder and Director of Fourth Hurdle, and previously Senior Manager of Global Health Outcomes at GlaxoWellcome, Adam has extensive experience leading economic evaluations of pre-launched and marketed products, developing submissions to NICE and the SMC, decision-analytic and Markov modeling, and in the use of health economics in reimbursement and marketing in continental Europe.

• Adam holds an MPhil in Economics, and a BA (Hons) in Philosophy, Politics and Economics from the University of Oxford.

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IMS & EVIDENCE OUTCOMESSOLUTIONS RESEARCH IMSHEALTH HEOR & ECONOMICS REAL-WORLD

EXPERTISE | IMS Charles Makin, MS, MBA, MM, BS • Charles Makin is Principal HEOR at IMS Health, focusing on naturalistic trials, adherence interventions, chart abstractions, patient-reported outcomes and other studies involving primary data collection.

• During a career that includes senior roles at the UnitedHealth Group and Wellpoint, Charles has led numerous studies involving database analyses, economic modeling, multi-country patient registries, systematic literature reviews and survey-based research.

• Charles holds a BS in Pharmacy from the University of Pune, India, an MS in Pharmacy Administration from Purdue University, Indiana, U.S. and an MBA in Marketing Management and a Master’s in Management, both from Goldey Beacom College, Delaware, U.S.

Frédérique Maurel, MS, MPH • Frédérique Maurel is Principal HEOR at IMS Health, with a particular focus on observational research and health economics studies.

• A skilled consultant and project manager, Frédérique has extensive experience in the economic evaluation of medical technologies gained in roles at ANDEM, Medicoeconomie, and AREMIS Consultants.

• Frédérique holds a Masters degree in Economics – equivalent to an MS – and completed a post-graduate degree equivalent to an MPH with a specialization in Health Economics at the University of Paris-Dauphine (Paris IX) as well as a degree in Industrial Strategies at the Pantheon-Sorbonne University (Paris I).

Joan McCormick, MBA • Joan McCormick is Principal at IMS Brogan, leading a team providing strategic advice to companies with new products coming to market and ongoing consultation on the rules for existing drugs post launch.

• Formerly Head of Price Regulation Consulting at Brogan Inc, Joan has supported many major pharmaceutical companies with the preparation of pricing submissions to the Patented Medicine Prices Review Board (PMPRB), gaining extensive insights into the operation of the Canadian pharmaceutical market.

• Joan holds a Bachelor’s degree in Life Sciences from Queen’s University in Kingston, Canada, and an MBA from the University of Ottawa, Canada.

Dana Morlet-Vigier, MD • Dr. Dana Morlet-Vigier is Principal HEOR at IMS Health, applying in-depth expertise and extensive experience in pharmaceutical pricing, reimbursement and market access to help clients meet the growing challenges of today’s increasingly complex product launch process.

• A medical doctor and INSEAD executive, Dana’s background spans 15 years in pharmaceuticals and includes roles in R&D, commercial, market access, strategy and government affairs at GlaxoSmithKline, Organon and 3M Pharma. She has worked on numerous pricing and reimbursement negotiations and designed and implemented national and international Phase II, III and IV studies across a wide range of therapy areas.

• Dana holds an MD from Bucharest Medical University, Romania and the Paris-Cochin Faculty, Paris, France.

Julie Munakata, MS • Julie Munakata is Principal HEOR at IMS Health, with a particular focus on global economic modeling, value development planning, and survey data analysis.

• An accomplished researcher and author of more than 25 original articles, Julie has extensive experience in managing clinical trials, health economic studies and decision analytic modeling work, gained in senior roles at ValueMedics Research LLC, the VA Health Economics Resource Center and Stanford Center for Primary Care & Outcomes Research, and Wyeth Pharmaceuticals.

• Julie holds an MS in Health Policy and Management from the Harvard School of Public Health and a BS in Psychobiology from the University of California, Los Angeles.

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IMS | EXPERTISE Michael Nelson, PHARM D • Dr. Michael Nelson is Senior Principal HEOR at IMS Health, with particular expertise in retrospective database research, prospective observational research, health program evaluation, and cost-effectiveness analysis.

• During a career that includes leadership roles in HEOR at PharmaNet, i3 Innovus, SmithKline Beecham, and DPS/UnitedHealth Group, Mike has gained extensive experience in health information-based product development, formulary design, drug use evaluation, and disease management program design and implementation.

• A thought leader in health economics for more than 20 years, Mike holds a doctorate in Pharmacy and a Bachelor of Science degree, both from the University of Minnesota College of Pharmacy. He also served as an adjunct clinical faculty member at the University of Minnesota whilst in clinical pharmacy practice.

Amy O’Sullivan, PHD • Dr. Amy O’Sullivan is Principal HEOR at IMS Health, with a particular focus on global economic modeling to support product reimbursement in jurisdictions around the world.

• Highly experienced in the economic evaluation of medical technologies, Amy’s background includes roles at Policy Analysis Inc. (PAI) and most recently in a senior capacity at OptumInsight (formerly i3 Innovus). She has led numerous pharmacoeconomic and outcomes research studies including cost-effectiveness analyses, budgetary impact analyses, burden-of-illness studies and piggyback economic evaluations. Her research spans a wide range of therapeutic areas, including autoimmune conditions, CV disease, CNS and behavioral health disorders, metabolic disorders, musculoskeletal conditions, oncology, respiratory disease and women’s health.

• Amy holds a PhD in Health Economics from the Johns Hopkins University Bloomberg School of Public Health, Baltimore, and a BA in Economics and English from Boston College.

Carme Piñol, MD, MSC • Dr. Carme Piñol is Principal HEOR at IMS Health, with more than 20 years experience in the pharmaceutical industry spanning clinical research, health economics and market access.

• Previously Head of Market Access for Spain at Bayer, a role that included pricing, HEOR, advocacy and institutional relations with the Regions, Carme is a Board member of the Spanish Association of Health Economics as well as the ISPOR Spain Regional Chapter and coordinator of the Pharmacoeconomics Interest Group of the Spanish Association of Medicine of the Pharmaceutical Industry (AMIFE). She has authored more than 60 communications in international and national congresses and more than 20 papers in peer-reviewed journals.

• Carme holds an MD from the Autonomous University of Barcelona, an MSc in Pharmacoeconomics and Health Economics from Pompeu Fabra University, an MSc in Health Research from Castilla-La Mancha University (UCLM), and an Executive Program Degree from ESADE Business School, Barcelona, Spain. She is currently completing a PhD in Health Research at UCLM.

Jon Resnick, MBA • Jon Resnick is Vice President HEOR and Real-World Evidence Solutions at IMS Health, leading the company’s global real-world evidence (RWE) business, including the development of RWE strategy, offerings, collaborations and foundational technologies to meet the RWE needs of healthcare stakeholders.

• A former Legislative Research Assistant in Washington DC and member of the Professional Health and Social Security staff for the US Senate Committee on Finance, Jon has 10 years consulting experience at IMS. He was most recently responsible for leading the European management consulting team and global HEOR business teams of 300 colleagues, advising clients on a wide range of strategic, pricing and market access issues.

• Jon holds an MBA from the Kellogg School of Management, Northwestern University, with a major in Management & Strategy, Finance, Health Industry Management and Biotechnology.

Vernon Schabert, PHD • Dr. Vernon Schabert is Senior Principal HEOR at IMS Health, with a particular focus on the assessment and validation of patient-reported outcomes (PRO) instruments, retrospective analyses of claims and survey databases, and primary data collection surveys.

• A founder and former President of Integral Health Decisions, Inc, Vernon has extensive experience in conducting claims analyses, creating custom administrative databases, developing business intelligence software, and leading national quality research projects, gained in roles with Thomson Reuters, Strategic Healthcare Programs LLC, and CIGNA HealthCare. His expertise spans numerous disease areas and diverse topics including medication adherence, in-patient safety and outcomes in post-acute care.

• Vernon holds a PhD in Personality and Social Psychology from Stanford University and a BA in Psychology from Princeton University.

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IMS & EVIDENCE OUTCOMESSOLUTIONS RESEARCH IMSHEALTH HEOR & ECONOMICS REAL-WORLD

EXPERTISE | IMS Núria Lara Surinach, MD, MSC • Dr. Núria Lara is Principal HEOR at IMS Health, with a particular focus on the design and coordination of local and international observational and patient-reported outcomes studies.

• A former practicing GP and clinical researcher, Núria’s experience spans roles in outcomes research at the Institute of Public Health in Barcelona and in Catalan Health Authorities, and consulting positions within the pharmaceutical and medical device industries focusing on medical regulatory and pricing affairs, pharmacoeconomics and market access strategies.

• Núria holds an MD (specializing in Family and Community Medicine in Barcelona), and a Master’s in Public Health from the London School of Hygiene and Tropical Medicine and London School of Economics.

Christian Thielscher, MD, MBA, PHD • Dr. Christian Thielscher is Principal HEOR at IMS Health and Professor for Health Economics at the FOM University in Germany, with expertise in clinical medicine, health economics, strategic consulting, business administration and innovative service development.

• Christian began his career in medical practice before moving into healthcare consulting at McKinsey, where he established one of the first disease management programs in Germany. He later founded GHX Europe where his work included developing the “health card” and the first German internet-based hospital guide.

• Founding president of the DGFM eV and editor of ‘Medizinökonomie’, Christian’s current research interests include quality in medicine and healthcare business management and marketing. He holds an MD from Bonn University, an MBA, and a PhD in Economics from the University of Frankfurt am Main, Germany.

Massoud Toussi, MD, MSC, PHD • Dr. Massoud Toussi is Medical Director HEOR at IMS Health with particular expertise in methodological and operational aspects of clinical research to assure the quality of interventional, observational and database studies.

• Previously Head of Global Clinical Research Operations at Cegedim, Massoud has also worked at the French High Authority for Health and various CROs as Project Lead, Scientific Manager and Operations Director. His experience includes defining and elaborating a new service process in drug safety signal detection and transmission.

• Massoud holds an MD from Mashad University in Iran, an MSc in Medical Informatics and Communication Technology from Paris IV, a PhD in Medical Informatics from Paris XIII University and a diploma in Transcultural Psychiatry from Paris Nord University.

Arnaud Troubat, PHARM D, MBA, MHEM • Dr. Arnaud Troubat is Principal HEOR at IMS Health. He has extensive consulting experience and special expertise in the development of registration dossiers and market access strategies across a large number of therapeutic areas.

• A pharmacist by training, Arnaud began his career at the French pharmaceutical industry association (LEEM), supporting members in understanding and interpreting the changing economical environment in France. He then spent a number of years in pharmaceutical affairs at ICI, leading regulatory work on registration submissions and reimbursement strategies, before subsequently moving into consulting. Most recently, he was Director at Carré-Castan Consultants, managing a team working for a wide range of pharmaceutical companies.

• Arnaud holds a Doctor of Pharmacy degree and an MBA from IAE Paris and a Master’s in Health Economics and Management from Paris-Dauphine University.

Jovan Willford, MBA • Jovan Willford is Principal Real-World Evidence Solutions at IMS Health, supporting growth strategy, offering development and commercialization of RWE solutions.

• Jovan’s background includes more than 10 years of strategic advisory experience across payers, providers, life science organizations and technology companies, including several cross-industry collaborations to advance quality and value of care delivery.

• Jovan holds an MBA from the Kellogg School of Management, Northwestern University, with majors in Management & Strategy, Managerial Economics and International Business, and an undergraduate degree from the University of Notre Dame with majors in Marketing and Philosophy.

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