Volume 22 Number 3 ▪ March/April 2020
Tu f t s C e n te r fo r t h e S t u d y o f D r u g D e ve l o p m e n t
IMPACT REPORT TUFTS UNIVERSITY
ANALYSIS & INSIGHT INTO CRITICAL DRUG DEVELOPMENT ISSUES
Drug developers respond to evolving clinical data demands with new strategies, tactics Only one-third of sponsors overall have implemented a formal data strategy ■■ More than two-thirds of all sponsors are using or piloting at least four different data sources in clinical trials. ■■ Initiating external data vendor relationships is rated as the most time-consuming data management task; data integration is cited as least time consuming. ■■ Medium and large sponsors are nearly twice as likely to have implemented a formal data strategy, compared to small sponsors. ■■ About a quarter of sponsors overall have formal data governance policies to manage data flow, compliance, and accessibility. ■■ Sponsors rely on a range of data tools and techniques to integrate and organize clinical trial data; clinical data hubs and repositories are the most widely used. ■■ Nearly three out of four sponsors are establishing data science disciplines or expanding the role of data scientists in their organizations.
D
igital transformation continues to unfold across the drug development enterprise, with growing demand for scientific data from diverse sources (e.g., patient reported outcomes; specialty labs; biomarkers; wearable devices; electronic medical information) and for operating data to oversee performance, quality, cost, and compliance. Sponsor appetite also is increasing for more sophisticated analytics (e.g., predictive; prescriptive; machine learning). Several recent Tufts CSDD analyses* have quantified dramatic expansion in clinical data volume and diversity during the last decade. This report builds on those earlier studies by examining how sponsor companies are managing and leveraging these data. It is based on a survey to determine how pharmaceutical and biotechnology companies are importing and integrating data, mapping data into analysis-ready data sets, curating data, and analyzing data, all to inform and support development decision-making and objectives. * See for example Tufts CSDD Impact Report January/February 2018;20(1) and Tufts CSDD Impact Report November/ December 2017;19(6)
More than two-thirds of all sponsors are using or piloting at least four primary data sources in clinical trials Share of companies using or piloting data sources Non-CRF Data
Direct Data Capture
Devices and Apps
Medical Images
EHR/EMR
-Omics Data
Small sponsors
86.4%
66.0%*
55.6%*
71.7%
25.6%*
27.9%
Medium & large sponsors
87.9%
83.8%*
82.1%*
69.1%
51.5%*
45.6%
Overall
87.3%
76.5%
71.4%
70.2%
41.4%
38.7%
* Subgroup differences significant at the P<.05 level
CRF = Case report form EHR/EMR = Electronic health record/electronic medical record Source: Tufts Center for the Study of Drug Development
■■ Non-CRF data are the most commonly used or piloted new source of data (87.3%) across small and large sponsors, but it also is the most frequent cited cause for database lock delays. -Omics data are the least often utilized (35.5%) and the least frequently cited cause of delays. ■■ EHR/EMR is the least utilized data source among small sponsors, while for large sponsors, -omics data are least utilized. ■■ Adoption of diverse data sources is generally higher among medium and large sponsors.
Almost one in five sponsors rate all seven key data tasks as "extremely time consuming" Survey respondents reporting specific data tasks as "extremely time consuming"
■■ Across all sponsors, data management related to initiating vendor relationships is rated the most time consuming among seven data management tasks assessed.
35% 30% 25% 20%
■■ More than twice as many small sponsors, compared to large sponsors, report transforming and mapping data as extremely time consuming.
15% 10% 5% 0% with data vendors
Transforming Performing data review and managing the mapping and load/publish the data cleaning process
Small sponsors
the data
Analyzing data
■■ Data curation is rated by all sponsors as the least time-consuming data management process.
Medium & large sponsors
Source: Tufts Center for the Study of Drug Development
About one-third of sponsors have implemented a data strategy Status of formal data strategy among sponsor companies
■■ 40% of all sponsors indicate they are considering or planning to implement a data strategy.
50% 45%
■■ Medium and large sponsors are nearly twice as likely to have implemented a formal data strategy, compared with small sponsors.
40% 35% 30% 25% 20% 15% 10% 5% 0%
Formal data strategy implemented Small sponsors
Examining or planning a formal data strategy Medium & large sponsors
■■ The vast majority (87%-93%) of sponsor companies perceive benefits of a formal data strategy, and those which have implemented a data strategy report faster time from Last Patient, Last Visit to Database Lock, compared to those with no formal strategy (36.1 days vs. 41.8 days).
Source: Tufts Center for the Study of Drug Development
Tufts CSDD Impact Report ▪ March/April 2020 | Page 2
One-fourth of sponsors overall have a formal data governance process in place Share of companies with data governance policies Clinical Operations Leads Governance
Data Management Leads Governance
R&D IT Leads Governance
Hybrid ClinOps and Data Management Lead
Small sponsors
20.8%
45.8%
4.2%
25.0%
Medium & large sponsors
12.8%
34.0%
8.5%
40.4%
Overall
15.5%
38.0%
7.0%
35.2%
Source: Tufts Center for the Study of Drug Development
■■ 16.2% of small companies report having a formal data governance process in place, with data management the primary function providing oversight. ■■ A higher percentage of medium and large sponsors (33%) report having a formal data governance process. ■■ Among medium and large sponsors with a formal data governance process, nearly 40% report using a hybrid mechanism across clinical operations and data management functions to provide oversight.
Sponsors rely on a range of data tools/techniques to integrate and organize clinical trial data Systems and tools used to manage data EDC
SAS-based Infrastructure Data
Clinical Data Hub/ Repository
Other Data
Non-CRF data
20.7%
46.6%
49.1%
8.6%
Direct data capture
50.9%
36.1%
33.3%
5.6%
Devices and apps
24.2%
42.4%
54.5%
4.0%
8.4%
27.4%
53.7%*
23.2%
20.3%
31.7%
44.3%
16.5%
3.9%
33.3%
41.1%
32.1%
Medical images EHR/EMR -Omics data
■■ Clinical data hubs and repositories are used most frequently to organize and integrate non-CRF, devices and apps, medical imaging, EHR/EMR, and -omics data. ■■ Sponsors rely most heavily on EDC tools to integrate data gathered through direct capture. ■■ SAS-based infrastructures are consistently the second most utilized tool to integrate and organize data.
* Subgroup differences significant at the P<.05 level
EDC = Electronic data capture Note: Respondents selected all responses that applied to their organization Source: Tufts Center for the Study of Drug Development
Nearly three out of four sponsors are establishing or expanding the data science role Management actions respondents reported taking on the data scientist role Not Building or Unsure
Incorporating Role Into Existing Data Management Function
Incorporating Role Into Another Functional Area
Establishing a Decentralized Data Science Role Across Functions
Establishing a Centralized New Data Science Function
Small sponsors
33.3%
33.3%
6.1%
21.2%
6.1%
Medium & large sponsors
20.4%
27.8%
7.4%
31.5%
13.0%
Overall
25.3%
29.9%
6.9%
27.6%
10.3%
Source: Tufts Center for the Study of Drug Development
■■ Most sponsors report that they are incorporating the data scientist role across existing functions, instead of creating a new function. ■■ A higher percentage of small sponsors, compared to medium and large sponsors, reports that they are not building the data scientist role in their organization or are unsure of the plans for the role. ■■ Medium- and large-sized sponsors are most likely to spread their data science roles across two or more functions (32%), while small sponsors are most likely to have these responsibilities concentrated within data management.
Tufts CSDD Impact Report ▪ March/April 2020 | Page 3
About this study
The analysis summarized in this report was based on an online global survey conducted by Tufts CSDD between October and December 2019. Analysis was based on responses from 149 companies. Of these, 47 were defined as ‘small sponsors' (initiating 1-5 clinical trials annually), 44 as ‘medium sponsors' (6-50 clinical trials annually), and 25 as ‘large sponsors’ (51-350 clinical trials annually). The remainder was comprised of 16 contract research organizations, 9 independent contractors, and 8 other. This analysis was conducted by Michael Wilkinson, MPH, project manager, Beth Harper, MBA project advisor, and Ken Getz, MBA, professor and deputy director, all of Tufts CSDD.
Definition of terms Clinical trial — A specific type of clinical study in which a medical intervention is tested against a placebo or an active control in human subjects. Clinical study is a broader term that includes other forms of human participatory research, such as pharmacokinetic, epidemiologic, and behavioral studies. Data strategy — Establishes a high level road map to guide and align an organization's use of data sources and how data will be used to support and complement development activity.
Devices and app data — Covers collection and management of clinical data from non-site personnel (subjects, participants, and caregivers) using mobile devices, including smartphone or tablet applications (e.g., electronic clinical outcome assessment), wearables, and sensors (e.g., glucose monitor, smart pill, remote chemistry, ambient sensors). Direct data capture — Includes direct entry of clinical data by site staff into a mobile application or EDC (electronic data
capture) system.
Electronic health record systems — Covers collection of clinical data for use in clinical research from site electronic health record systems.
Non-CRF data — Includes collection and transfer of data in electronic format from internal sponsor sources (e.g., specialty laboratories) or external vendors (e.g., laboratory results, imaging, electrocardiogram, randomization, drug accountability) into clinical research data repositories/warehouses without entering the data on a case report form (CRF). -Omics data — Refers to data characterizing biologic dynamics in such areas as genomics, proteomics, and metabolomics.
About the Tufts Center for the Study of Drug Development The Tufts Center for the Study of Drug Development at Tufts University provides data-driven analysis and strategic insight to help drug developers, regulators, and policy-makers improve the efficiency and productivity of pharmaceutical R&D. Tufts CSDD also offers professional development courses and hosts workshops and public forums on a wide range of drug development issues. Tufts Center for the Study of Drug Development
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Tufts CSDD Impact Reports are published by the Tufts Center for the Study of Drug Development six times a year; Kenneth I Kaitin, PhD, editor. © 2020 Tufts University. All rights reserved. Tufts CSDD Impact Reports may not be reproduced, transmitted, or distributed by any means, mechanical or electronic, in whole or in part, without written permission of the Tufts Center for the Study of Drug Development.
Tufts CSDD Impact Report ▪ March/April 2020 | Page 4