Setting New Standards for Performance Measurement in Decentralized Trials

Despite the growth in decentralized clinical trials (DCTs) enabled by advanced technologies that allow data to be collected directly from participants in the home or wherever they are, standards and approaches for measuring study success have not kept pace. Remote technologies allow for data to be collected and uploaded to electronic platforms in realtime, yet many managers of clinical research studies are continuing to use study performance metrics designed around traditional, site-based studies.

These approaches, which are built on regular participant visits, participant outcome report diaries (often still done with pen and paper), and on-site diagnostic testing are not sufficient for tracking performance of the many remote activities going on in a DCT. Further, they do not account for the near-constant availability of up-to-the-minute data that can be produced through technologies like wearable medical devices.

Effective approaches to study performance measurement must match the studies themselves in terms of using advanced technologies and modern approaches. This report will discuss THREAD’s approach to study performance monitoring, in particular the ability to track study performance in real-time combined with the use of advanced predictive analytics, providing users with full visibility into how their study is performing in several key categories while simultaneously showing where the data is likely to go in the future. With this level of comprehensive insight, sponsors and CROs can identify potential problem areas and make the necessary changes to get their study back on track.

State of Metrics for DCT

It is estimated that one quarter of all clinical studies have included a decentralized component or components in the past year. Interest in decentralized, or virtual clinical trials – as well as hybrid study models (studies conducted with a combination of sites and remote activity) is increasing rapidly, as a growing number of sponsors are seeking to launch and operate decentralized clinical trials (DCTs). Industry watchers expect the market for DCTs to reach $10 billion by 2026, growing by 6.5% CAGR over the next five years. In 2020, COVID-19 catalyzed the already growing rate of DCT implementations as many site-based studies were transitioned to DCTs out of concern for participant safety and trial continuity.

The FDA defines DCTs as the decentralization of clinical trial operations where technology is used to communicate with study participants and collect data. In this way, DCTs can eliminate some or all the need for participants to visit brick and mortar sites. Many traditional components within a trial can be replaced with a decentralized approach. For example, clinician reported outcome assessments (ClinROs) can be performed via a telehealth visit using technology that meets the required regulatory requirements. Blood draws and other lab work can be done by home health providers in the home setting. Many vital signs and actigraphy information can be obtained by sensors directly from the participant and transmitted electronically.

In DCTs, data is gathered remotely from the patient regardless of their physical location. Typical DCTs may involve:

• ClinROData collected using telehealth/virtual visits
• eConsent data
• Electronic Patient Reported Outcome (ePRO) data
• Data such as physical exam findings collected from home health providers
• Data from in-home medical devices such as a blood-pressure cuff
• Data from wearable sensors, such as a smart watch or mobile ECG
• Data from other electronic outcome reports (e.g.observers)
• Direct to Patient study drug shipping and accountability

DCTs offer several advantages over traditional, site-based trials. DCTs help sponsors optimize patient recruitment, allowing them to seek out and enroll patients with few if any geographical limitations – patients are not required to live within a certain distance from a clinical site. There are also clear benefits from using remote technologies to collect data directly from patients, as they allow data to be gathered more frequently and made available in near real-time for study team review. The technologies also help to ease patient and caregiver burden and facilitate engagement, providing support features like medication reminders and easy to use channels for them to contact their clinicians with questions or concerns.

Despite these new methods for collecting patient data remotely, many studies still rely on older methods for measuring study success. In traditional, site-based clinical studies, patients make regular visits and undergo clinical examinations (which can include physical exams by a physician, imaging, blood draws and other diagnostic activities) and interviews where study team members review patient diaries and ask questions regarding study activities like medication dosing, changes in symptoms, and adverse events – as well as any other pertinent information relevant to the study. Applying the same standards of measurement used for in-clinic data collection to DCTs risks missing the big picture and true value of DCTs versus fully site-based studies. As an example, in traditional site-based studies, one measure of success might deal with enrollment rates per site, per month. In a DCT, digital recruitment platforms have to be measured differently since the range of potential patients is larger.

Technologies are being used to collect data directly from participants and results are seen in real-time

Another key difference is that paper diary-based data collection, standard site-based studies, is now replaced with data collection on the participant’s own device allowing study teams to measure completion in real time. This eliminates the ‘parking lot syndrome’ where participants would complete their diary in the clinic parking lot prior to a site visit. Applying an older (e.g., paper-based) approach to measurement in a decentralized or hybrid trial, largely, eliminates much of the benefit of running these studies. Technologies are being used to collect data directly from participants and results are seen in real-time. To realize the advantages provided by such technology, effective study monitoring and measurement need to work just as quickly so that stakeholders can review participant data as it is available and act on it if needed, when problems are identified.

New approaches require new metrics to accurately assess their effectiveness. For approaches powered by advanced technologies like artificial intelligence (AI), machine learning and others, effective measurement tools must be similarly sophisticated while, simultaneously, striving to be intuitive and easy for users across stakeholder groups to understand.