Keywords: Bayesian Clinical Trial Design, Historical Data, Model Averaging, Sequential Monitoring, Skeptical Prior
In this talk we provide an overview of Bayesian sequential monitoring with application to pediatric clinical trials. In such trials, patients are continually enrolled and their data are analyzed as often as is desired or feasible until a hypothesis has been proven or disproven, or until the allocated resources for the trial have been exhausted (i.e., the maximum sample size or study duration has been reached). Such an approach is particularly appealing in the case of difficulty-to-enroll populations such as adolescents and children. A Bayesian sequentially monitored trial does not require a pre-specified sample size or number of analyses. For proving efficacy in a sequentially monitored trial, the Bayesian collects data until the evidence in favor of the investigational treatment is substantial from the perspective of an a priori skeptical judge who doubts treatment efficacy and the possibility of large treatment effects. The Bayesian approach naturally allows for the incorporation of prior information when determining when to stop patient accrual and ultimately in evidence evaluation once the complete data are available. We will give easy-to-understand examples for how Bayesian methods can be applied in this setting of pediatric trials.