Poster presentation at the Missouri Technology conference (created 2010-10-04).

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I will be presenting a poster about the Bayesian model for accrual at the Missouri Technology conference in Columbia, Missouri. There was some confusion about this, partly because I submitted an abstract at the last minute. Here is the abstract that I turned in.

AccrualMaster: Software for planning and monitoring accrual rates in clinical trials
Stephen D. Simon, PhD¹², Byron Gajewski, PhD^3
1P.Mean Consulting, Leawood, KS
²Department of Biomedical and Health Informatics, UMKC, Kansas City, MO
³Center for Biostatistics and Advanced Informatics, KUMC, Kansas City, KS

Abstract: The most common reason why clinical trials fail is that they fall well below their goals for patient accrual. Researchers will frequently overpromise and underdeliver on the number of patients that they can recruit during the proposed time frame. The result is studies that take far longer than planned and/or that end with fewer patients than planned. This raises serious economic and ethical issues. We have developed a Bayesian model for accrual that will encourage careful planning of accrual rates as well as allow regular monitoring of accrual patterns during the conduct of the clinical trial. We have developed software in R that can show graphically the expected duration of the trial under initial planning estimates of accrual rates and that can adjust those accrual rates as the trial progresses by combing the actual accrual data with the prior beliefs of accrual. This software can be used by individual researchers, by Institution Review Boards during their continuing review of approved projects, and by Data Safety and Monitoring Boards during their interim analysis. We are working on extensions of the software to multi-center trials, to assessing the impact of refusal rates and losses due to exclusion criteria, and to non-uniform accrual rates (e.g., accrual rates in a trial expected to have a slow startup period). We are looking for support and collaborators to make the software available on the web on an R server using a simplified front-end interface, to test the software prospectively in a series of clinical trials, and to support research on the extensions of the Bayesian accrual model.

I will re-use a poster that I had developed for an earlier conference, the Missouri Regional Life Sciences Summit. Here is the abstract I submitted to that conference and a PDF of that poster. There are more technical details in an earlier poster presented at the 2007 Joint Statistical Meeting. The definitive reference is Gajewski BJ, Simon SD, Carlson SE. Predicting accrual in clinical trials with Bayesian posterior predictive distributions. Stat Med. 2008 Jun 15;27(13):2328-40. [Medline] [Abstract].