The key original contribution of this work is the use of a Bayes optimisation framework for the decision made at the interim analysis of Adaptive Enrichment trials. Adaptive Enrichment designs make efficient use of pre-identified patient sub-populations. They begin by recruiting from all eligible patients, then at a pre-planned interim analysis select which sub-populations will be recruited from for the remainder of the sample. We ensure strong control of the Familywise Error Rate whichever sub-populations are selected by constructing an overall hypothesis testing structure using both closed testing procedures and combination tests. This allows us to make interim decision by any method we choose. We find the Bayes optimal decision, recruiting the remainder of the trial to optimise the Bayes expected gain of the trial. We compare the Bayes optimal Adaptive Enrichment trials with fixed sampling designs to understand the overall advantage of using adaptive trials.This optimisation framework is very flexible, we evaluate the performance of Bayes optimal Adaptive Enrichment designs for different forms of data: delayed responses, longitudinal analysis and discuss the extension of these methods to survival data. Through this we see that although the information at the interim analysis is reduced the adaptive trials still offer some benefit. Additionally we investigate what may happen when we alter the pattern of recruitment of the Adaptive Enrichment trials, showing that adaptation may be useful in a broad range of scenarios.
|Date of Award||2 Nov 2017|
|Supervisor||Christopher Jennison (Supervisor) & Simon Harris (Supervisor)|