Key Drivers for Transformation

Availability of patient-generated data from electronic health records, wearables, and other remote monitoring devices has led to an exponential increase in both data volume and variety. Additionally, personalized medicine, difficulty accessing patient populations in rare diseases, and the need to reflect diversity within clinical trials are driving uptake of innovative trial approaches including adaptive designs, enrichment strategies, and master protocols. Adoption of these designs can improve trial efficiency and patient outcomes, but also lead to increased data complexity, and, in turn, demand new skills and expertise from clinical programmers, clinical data reviewers, data managers, and biostatisticians.

In today’s speed-driven trials, modern clinical teams increasingly expect real-time, or near real-time data access to detect any issues or trends that may impact the trial outcome and to achieve informed decision-making. Timely data access is also a vital component of facilitating complex trial designs which require ongoing monitoring and planned adaptations. As advancements in technology and data analytics are making it easier to collect, analyse, and share real-time data in clinical trials, biometrics teams are driven to adapt their infrastructure and processes to accommodate this trend.

The trend towards patient-centricity in clinical trials has significant implications for biometrics functions. Decentralized and hybrid trial models, which aim to ‘meet the patient where they are’ involve the remote collection of real-world data from patients in their homes, and the use of wearable technology and other digital tools. This momentum brings with it a requirement for biometrics teams to manage and analyze large volumes of data from multiple sources, often in real time. The sheer volume of data requires a greater focus on what data is truly ‘critical’ with biometrics teams applying risk-informed approaches, and robust technologies and workflows to manage external data that is collected outside EDC (Electronic Data Capture).

Data scientists also need to think beyond traditional clinical trial endpoints and consider outcomes that reflect the patient’s experience with the disease or treatment. Ensuring participant diversity has also become a pressing priority and data-driven strategies are key to overcoming underrepresentation by providing real-time insights about enrollment.

At the same time, the imperative to give patients with unmet needs faster access to new medicines, is ushering in adoption of ‘complex’ clinical trials better suited to answering the research question of interest at speed. For example, master protocols, enrichment strategies, and innovative adaptive designs are now commonly used approaches for generating clinical data with greater operational efficiency and reduced patient burden.1

Clinical trials require the investment of considerable resources from all stakeholders. As clinical trials become more ambitious, incorporating larger sample sizes, longer trial durations, and more endpoints, the need for efficiency to optimize processes and maximize the chances of overall trial success is heightened.2 https://www.efpia.eu/news-events/the-efpia-view/blog-articles/complex-clinical-trials-a-decade-of-innovation-in-clinical-research/ https://www.globenewswire.com/en/news-release/2021/01/12/2157143/0/en/Rising-Protocol-Design-Complexity-Is-Driving-Rapid-Growth-inClinical-Trial-Data-Volume-According-to-Tufts-Center-for-the-Study-of-Drug-Development.html

Traditional manual processes of data collection, analysis, and reporting are unwieldy and time-consuming. And the consequences of any errors and inefficiencies are severe- leading to costly delays and potential regulatory issues.

By transforming processes to better leverage digital technologies and automation, biometrics functions can accelerate cycle times, maintain regulatory compliance and reduce costs while maximizing the quality and value of critical clinical data.