School of Mathematical Sciences

Statistical inference in response-adaptive trials menu

Statistical inference in response-adaptive trials

D. S. Robertson, University of Cambridge
Thu, 07/12/2017 - 16:00
Queens' W316
Seminar series: 

Clinical trials typically randomise patients to the different treatment arms using a fixed randomisation scheme, such as equal randomisation. However, such schemes mean that a large number of patients will continue to be allocated to inferior treatments throughout the trial. To address this ethical issue, response-adaptive randomisation schemes have been proposed, which update the randomisation probabilities using the accumulating response data so that more patients are allocated to treatments that are performing well.

A long-standing barrier to using response-adaptive trials in practice, particularly from a regulatory viewpoint, is concern over bias and type I error inflation. In this talk, I will describe recent methodological advances that aim to address both of these concerns.

First I give a summary of a paper by Bowden and Trippa (2017) on unbiased estimation for response adaptive trials. The authors derive a simple expression for the bias of the usual maximum likelihood estimator, and propose three procedures for bias-adjusted estimation.

I then present recent work on adaptive testing procedures that ensure strong familywise error control. The approach can be used for both fully-sequential and block-randomised trials, and for general adaptive randomisation rules. We show there can be a high price to pay in terms of power to achieve familywise error control for randomisation schemes with extreme allocation probabilities. However, for proposed Bayesian adaptive randomisation schemes in the literature, our adaptive tests maintain or increase the power of the trial.