Standardized performance metrics that are actionable drive transparency and process improvement
goBalto, announced today the March release of the ChromoReport, a quarterly analytical discussion on study startup, representing over 70 percent of clinical trial sites in phase II & III of the Top 25 pharma companies.
There is a distinct rallying cry emanating from the clinical trials sector on reimagining how studies should be launched. Because study startup is a lengthy and complicated process, it is a major cause of study timelines and budgets sliding off course. To minimize this challenge, virtually every aspect of how a study starts has been scrutinized, and then reported in a litany of journal articles, webinars, conferences, and blogs. But has anyone considered the role of seasonality? Is it better to start a study in March rather than May? How about September rather than November?
Seasonality may be something to consider, especially since clinicaltrials.gov reports that more than 46,000 clinical trials are in recruitment mode. And for too long, the statistics evaluating study startup performance have been sobering. Results of a 2017 survey conducted by the Tufts Center for the Study of Drug Development, Start-up Time And Readiness Tracking (START) II, found that 28% of participating contract research organizations (CROs) reported the site selection process was taking longer than it did three years earlier. Similarly, 15% of CROs and 35% of sponsors involved in the survey claimed that study startup cycle time was more prolonged than three years earlier. Another key finding of the Tufts study was that 11% of sites were never activated, a statistic that has remained nearly unchanged for more than a decade.
Fortunately, cloud-based workflow-driven solutions are being implemented among the largest pharmaceutical sponsors and CROs, and they are making positive inroads into the various processes that define study startup. Yet, seasonality is an issue worth exploring, and may complement the ongoing move toward purpose-built electronic solutions.
There are, of course, clinical trials with an obvious seasonal focus, such as seasonal allergic rhinitis, seasonal affective disorder, or the flu. But, considering some of the industry's top challenges — recruiting and enrolling patients, and competing for the best investigative sites — it may be useful to evaluate the seasonal factor beyond these obvious few to include a wide array of therapeutic areas.
Only a small number of published studies address seasonality. A seminal paper by Haidich and Ioannidis researched this subject to better understand difficulties in patient recruitment for large multicenter HIV clinical trials. The authors hypothesized that these challenges may be tied to the fact that sites belonging to the AIDS Clinical Trials Group (ACTG) network were likely to be performing several large clinical trials concurrently. As a result, the authors sought to identify possible predictors of the overall rate of enrollment in these large studies. Seasonality was one of those predictors. To research this possibility, they performed analyses on two datasets from the large database of all HIV clinical trials from the ACTG, ranging from October 1986 - November 1999. This evaluation included nearly 70,000 patient entries from 475 studies.
Results showed that patient enrollment varied significantly among different months, (p < 0.001), with recruitment peaking in spring (March, April) and late fall (October, November). It slowed during the winter months and in September. The reasons given for this seasonal effect were complex and were tied to whether a study's sample size exceeded 1,000 patients.
Interestingly, improved enrollment performance over time seemed to attenuate the seasonal effect to some degree. This makes the nexus of electronic study startup tools and seasonality a fascinating subject for discussion. In particular, it raises the question as to whether clinical trials should be staggered in order to meet enrollment timelines, and at the same time, embrace purpose-build study startup tools to improve the allocation of internal resources.
What do the industry metrics have to say about seasonality in clinical trials?
Patient's Can't Wait
"Turning big data into big insights requires analytics that are actionable. Performance metrics must be data-driven, standardized across studies, indication, and therapeutic areas, and timely. They must also, importantly, facilitate a forum for discussion."
Jeff was formerly VP Clinical Innovation and Implementation at Eli Lilly and Company
President and Founder
"Utilizing overarching cycle times within your own company or team is only the beginning of creating the best results for your company. To truly begin to move toward 'best in class' or 'industry leading' you need to consider your processes and cycle times from a bottleneck perspective, as well as industry standard. By thoroughly understanding the data you will support your teams to make the best decisions to improve study startup, ensuring that the goal of getting protocols to patients faster is achieved, which represents the first step in competing a study on or ahead of schedule."
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