How Decentralized Approaches Mitigate Pandemic-Driven Enrollment Challenges
An Analysis Comparing Subject Enrollment and Discontinuation in Decentralized and Traditional Clinical Trials from January-September 2020
Introduction
The COVID-19 pandemic has had a transformative impact on the delivery of clinical trials. The implementation of social distancing, quarantines, and stay-at-home orders has resulted in unprecedented delays and disruptions to operations across the globe, and thousands of trials have been suspended or stopped in 20201. The pandemic has also served as an accelerant for the adoption of technology-based innovations that have steadily been gaining traction for years. Perhaps the most significant evolution in clinical trial design and delivery throughout 2020 and into 2021 has been the wide-spread adoption of technologies to facilitate decentralization of clinical trials via solutions like digital recruitment, virtual visits and eCOA captured via telehealth.
According to the Society for Clinical Data Management (SCDM), one of the primary risk areas associated with clinical trials has been the dependency on research staff being continuously and physically on-site. Simple deviations to staff schedules, such as challenges to daily commute or personal leave, can cause delays to data entry, and to site initiations and enrollments2. In 2020, COVID-19 has transformed these constant background risks into pressing global issues. Several studies have sought to understand the broad impact of COVID-19 on clinical trials, but few have shown how this impact has differed between traditional site-based trials and those employing decentralized approaches for trial data collection. Now, more than a year after initial quarantines and stay-at-home orders took effect, we are able to start to understand whether the application of decentralized methods has allowed certain clinical trials to overcome these challenges.
The analysis described is not intended to be a comprehensive assessment of the impact of decentralized approaches on clinical trial success. The authors selected two (2) trial performance metrics pertinent to clinical trial success and conducted the analysis with the goal of providing relevant information for industry, as research sponsors look to integrate lessons from 2020 into future clinical trial designs. Additional analyses will be conducted in the future to supplement the analysis described below to more comprehensively and holistically address how decentralized trials fared in other performance focus areas against traditionally designed trials in 2020.
Scope of Analysis and Methodology
To better understand the impact of decentralized approaches on clinical trial success during pandemic-driven shutdowns, THREAD and Lokavant conducted an analysis to understand how clinical trials delivered on the THREAD DCT Platform, and traditional clinical trials with traditional site-based patient visits, adapted to the pandemic over the first three quarters of 2020. This time period allowed for observation of pre-COVID performance in January and February, immediate response to widespread quarantine and shelter-in-place orders in March and April, and subsequent peri-COVID performance through September.
Perhaps the most significant evolution in clinical trial design and delivery throughout 2020 has been the wide-spread adoption of technologies to afacilitate decentralization of clinical trials
For the initial analysis, THREAD provided operational performance data from ongoing decentralized trials within the selected time window and two (2) metrics corresponding with available THREAD operational performance data were selected from Insight, Lokavant’s clinical operations benchmarking solution: Subject Enrollment Rate and Subject Discontinuation Rate. The relevant Industry Benchmark, taking into account key parameters of the THREAD Data Set, was calculated utilizing Insight, which draws on comprehensive operational data on over 1,300 historical trials and near real-time data on ongoing studies. To avoid any complications with direct comparison of absolute values across THREAD and Industry trial datasets, respective datasets were min/max normalized and results are shown relative to respective January reference points.
Both Data Sets comprise a mix of study phases, therapeutic areas, and geographies. The THREAD Data Set includes hybrid and fully decentralized trails, and one registry study.