The Cytel blog keeps you up to speed with the latest developments in biostatistics and clinical biometrics.
Clyde Haberman, a columnist for the New York Times, once commented on the remarkable consistency of train arrival times on the Tokyo subway: "Every station lists the scheduled arrival times: 9:01, 9:04, 9:08 and so on. I lived in that city for five years...I never saw a train arrive so much as a minute late, not once. A posting of 9:01 meant 9:01." . Such predictability is rarely observed in the messy world of clinical operations, yet many study plans are formulated like a Tokyo subway timetable. In a previous blog entry , we cited an example trial that targeted 1,800 patients across 50 sites over a 10-month period. Let us examine three underlying assumptions in this plan, with the help of a modeling and simulation tool.
Two insightful papers from Applied Clinical Trials should be of interest to many clinical trial planners. The first by Kenneth Getz describes the problem of enrollment performance, while the second by Matthew Kibby proposes a potential solution.
Getz reports a study providing recent estimates of industry-wide rates of enrollment delays . In 2012, the Tufts Center for the Study of Drug Development (CSDD) requested data from 10 pharmaceutical companies and two CROs. The combined database covered nearly 16,000 investigative sites involved in 151 clinical trials from years 2008-2010. A few significant findings are worth highlighting:
Cytel has published a new whitepaper on Monte Carlo Simulations for Patient Recruitment, which illustrates how a technique already popular within industrial and business environments is now changing the game of data-driven patient enrollment forecasting.
Midway through a trial is a terrible time to realize that you need a new strategy to complete the study. Sadly, it is typically midway through a trial when drug supply, patient recruitment and budget all tend to deviate from the planned development path. Sometimes this is because the initial plan utilized idealized assumptions, (i.e. non-random patient enrollment), which failed to give the desired ballpark estimate of timelines and resource constraints. Other times, responding to unexpected operational or statistical challenges might have proven difficult due to inflexible trial designs .
We have spoken in some depth about how thorough planning and room for flexible decision-making can avoid some of these potential difficulties   . However, sometimes the specter of trial discontinuity arises anyway. Here is a scenario which recently confronted the Cytel Consulting Team.
Monte Carlo Simulations for Patient Enrollment: A Presentation by the Director of Pfizer's Feasibility Center for Excellence
Recently, we published an interview with Chris Conklin, the Director of the Center for Feasibility Excellence at Pfizer. During the interview, Chris spoke about how his team navigates the complex terrain of trial planning and patient recruitment, and achieves those high flying enrollment milestones for each and every trial. His key message was to utilize modern methods in data-driven feasibility studies, augmenting historic and site-level data with new techniques in forecasting.
Since our interview, Chris gave a talk at the annual SCOPE conference, in which he divulged a few more tips on obtaining consistent patient enrollment figures. An important feature was the use of Monte Carlo simulations, a popular tool from industrial and business operations, which is now gaining popularity amongst clinical operations specialists.
Monte Carlo simulations are easy to implement with the right tools, and yet can achieve target enrollment with 99% confidence. You can find below, a simple explanation of how this method works. Chris's slides (attached) contain a case study.
Data driven decision-making can ensure that every feasibility team achieves its enrollment milestones. By transforming how pharmaceutical companies and CROs conduct feasibility studies, new techniques in recruitment planning are affecting every aspect of trial planning and clinical development strategy.
We sat down with Chris Conklin, the Director of the Feasibility Center for Excellence at Pfizer (pictured below) to discuss innovative ways to think about enrollment factors, and how he uses high-precision forecasting tools like Cytel’s EnForeSys®.
Every clinical trial requires some manner of trial forecasting, normally for feasibility and patient enrollment. However, studies reveal that more than 50% of clinical trials fail to meet enrollment targets, and that enrollment is the most commonly cited reason for Phase 3 trial discontinuation .
Last week we released an infographic on why Phase 3 trials fail. The numbers, while eye-opening, did not capture a related and equally important issue: Why are so many late stage clinical trials discontinued?
Nearly 50% of all Phase 3 trials that are submitted to the FDA fail upon first submission . However, 25% of all trials that begin are never even submitted for review . According to a 2014 JAMA study, nearly 40% of these discontinuations cite poor enrollment as the primary reason for stopping a trial. Needless to say, the costs of discontinuity are significant.