Sergio Bacallado (University of Cambridge)

Three stories on clinical trial design

The design of randomised clinical trials is one of the most classical applications of modern Statistics. The first part of this talk has to do with adaptive trial designs, which aim to minimise the harm to study participants by biasing randomisation toward arms that are performing well, or by closing experimental arms when there is early evidence of futility. We first propose a class of Bayesian uncertainty-directed trial designs, which aim to maximise information gain at the trial's conclusion, and we show in applications to various types of trial that it has superior operating characteristics when compared to simpler adaptive policies. In a second section, I will discuss the use of reinforcement learning algorithms to approximate Bayes-optimal policies given a prior for the treatment effects and a utility function combining outcomes for participants and the uncertainty of treatment effects. The last part of the talk will consider the possibility of sharing preliminary data from trials with patients and physicians who are making enrollment decisions. This practice may be in line with a trend toward patient-centred clinical research, but it presents many challenges and potential pitfalls. Through a simulation study, modelled on the landscape of Glioblastoma trials in the last 15 years, we explore how such 'permeable' designs could affect operating characteristics and the statistical validity of trial conclusions.

Joint work with Lorenzo Trippa, Steffen Ventz, and Brian Alexander