Inference from blinded data in randomized clinical trials

Abstract

With blinded data, several authors have concluded that there is a negligible chance of inferring a non-null treatment effect. The recent Food and Drug Administration (FDA) draft guidance document on adaptive trials, by encouraging blinded sample size reestimation, implies the same. We derive methods to investigate whether the probability of inferring a treatment effect is much larger than previously thought, and whether that is of concern. A statistic is developed that contributes to improving signal detection. Additionally, trials that are overpowered, for reasons external to powering the primary objective, further strengthen the chance of finding a signal. An example of data from a clinical trial shows how revealing a blinded analysis can be. The ability to infer a non-null effect while a blinded trial is ongoing is a serious matter. The methods apply to superiority trials and are of limited use for non-inferiority or equivalence trials. It is important, therefore, that guidance documents include clear language to limit or prevent inference from blinded data to maintain trial integrity. Simple steps are proposed to make inference difficult.