Frequentist and Bayesian Sequential Experiments for Pandemic-Disrupted Clinical Trials, With an Application to the United Kingdom’s ‘DISC’ Trial

We use innovations from the frequentist and Bayesian decision-theoretic sequential experimental design literature to study whether, and when, recruitment to a pandemic-disrupted clinical trial should restart. We consider four frequentist and two Bayesian designs, two of which are new, and apply them to data from the UK's 'DISC' trial, a publicly-funded trial whose recruitment was seriously disrupted by the COVID-19 pandemic. The results delivered by all six designs concur with the DISC trial's results concerning treatment superiority. However, they do so with different levels of information, owing to different recommendations about restarting recruitment. Referencing work on the seven virtues of good statistical practice, we consider how confronting the same experimental data with a range of statistical models could assist policy-makers tasked with managing non-pandemic clinical trials during a future pandemic.