My boss at UMKC (I’m part-time at UMKC and part-time independent statistical consultant) asked me for one of those “summarize the research you’ve been working on” so she could mention all the work being done by our Department for a talk she’s giving. Recently, I’ve been focused almost exclusively on one thing, and although she knew it very well, I sent her a summary anyway. Then, I thought, why not share the same summary on my blog. Maybe you’re curious or maybe you might be interested in collaborating. So here’s my summary about my work on Bayesian models for patient accrual in clinical trials.
Slow accrual of patients in clinical trials is the most critical factor hampering the completion of clinical trials and represents significant threat to the integrity of research. Delays in the completion of trials damage our ability to evaluate novel therapies and interventions in a timely fashion and undermine the Nuremberg Code’s mandate that research should “yield fruitful results for the good of society.” In collaboration with researchers at Kansas University Medical Center, Steve Simon has helped develop a Bayesian model for patient accrual that incorporates historical information from previous trials. As the trial progresses, the Bayesian model updates the predicted probability of successful completion of the trial on time with the recommended number of subjects. The model also allows researchers to assess the probability of meeting a “fallback” option which lengthens the trial duration, decreases the proposed sample size, or possibly both.
We’re currently seeking NIH funding to extend the Bayesian model and to write accessible and easy to use software for accrual that can run on a website, a stand-alone computer, or a smartphone/tablet.