Adaptive methods for Bayesian time-to-event point-of-care clinical trials

Point-of-care clinical trials are randomized clinical trials designed to maximize pragmatic design features. The goal is to integrate research into standard care such that the burden of research is minimized for patient and physician, including recruitment, randomization and study visits. When possible, these studies employ Bayesian adaptive methods and data collection through the medical record. Due to the passive and adaptive nature of these trials, a number of unique challenges may arise over the course of a study. In this dissertation, adaptive methodology for Bayesian time-to-event clinical trials is developed and evaluated for studies with limited censoring. Use of a normal approximation to the study parameter likelihood is proposed for trials in which the likelihood is not normally distributed and assessed with respect to frequentist type I and II errors. A previously developed method for choosing a normal prior distribution for analysis is applied with modifications to allow for adaptive randomization. This method of prior selection in conjunction with the normal parameter likelihood is used to estimate future data for the purpose of prediction of study success. A previously published method for future event estimation is modified to allow for adaptive randomization and inclusion of prior information. Accuracy of this method is evaluated against final study numbers under a range of study designs and parameter likelihood assumptions. With these future estimates, we predict study conclusions by calculating predicted probabilities of study outcome and compare them to actual study conclusions. Reliability of this method is evaluated considering prior distribution choice, study design, and use of an incorrect likelihood for analysis. The normal approximation to non-normally distributed data performs well here and is reliable when the underlying likelihood is known. The choice of analytic prior distribution agrees with previously published results when equal allocation is forced, but changes depending on the severity of adaptive allocation. Performance of event estimation and prediction vary, but can provide reliable estimates after only 25 subjects have been observed. Analysis and prediction can reliably be carried out in point-of-care studies when care is taken to ensure assumptions are reasonable