P.Mean: Plug for accrual research (created 2008-07-24).

I received a request for use of material from my old website. It's a bit tricky right now, but I hope to have things resolved soon. The person inquiring was the owner of a company that specializes in clinical research and clinical data management. I thought it wouldn't hurt to mention some of the work that Byron Gajewski and I have done in accrual rates. Here's what I wrote.

I'd also like to mention that an active area of research for me right now is planning and monitoring accrual rates in clinical trials. It is a rather simple approach using Bayesian prior distributions. The nice thing about a Bayesian approach is that researchers will almost always have some idea about accrual rates. If you can formally elicit this information, it allows you to simulate the duration of the clinical trial. Then, as patients start joining the study, you can update the prior distribution to get a posterior prediction about the duration of the trial. The posterior prediction is a weighted average of the prior information and the actual accrual data. When knowledge about the accrual pattern is strong (perhaps because the researcher has conducted many similar trials), this prevents you from overreacting to a bit of early bad news. But when the knowledge about the accrual pattern is weak (perhaps because the researcher is working with a novel patient population), the Bayesian posterior distribution will allow you to react quickly at the first hint of trouble. I have a fair amount of material about accrual on my old website. Take a look at http://www.childrens-mercy.org/stats/category/AccrualProblems.asp.

Creative Commons License This work is licensed under a Creative Commons Attribution 3.0 United States License. This page was written by Steve Simon and was last modified on 2010-04-01. Need more information? I have a page with general help resources. You can also browse for pages similar to this one at Category: Accrual problems in clinical trials.