Stopping rules and estimation problems in clinical trials

Stopping rules in clinical trials can lead to bias in point estimation of the magnitude of treatment difference. A simulation exercise, based on estimation of the risk ratio in a typical post-myocardial infarction trial, examines the nature of this exaggeration of treatment effect under various group sequential plans and also under continuous naive monitoring for statistical significance. For a fixed treatment effect the median bias in group sequential design is small, but it is greatest for effects that the trial has reasonable power to detect. Bias is evidently greater in trials that stop early and is dramatic under naive monitoring for significance. Group sequential plans lead to a multimodal sampling distribution of treatment effect, which poses problems for incorporating their estimates into meta-analyses. By simulating a population of trials with treatment effects modelled by an underlying distribution of true risk ratios, a Bayesian method is proposed for assessing the plausible range of true treatment effect for any trial based on interim results. This approach is particularly useful for producing shrinkage of the unexpectedly large and imprecise observed treatment effects that arise in clinical trials that stop early. Its implications for trial design are discussed.