Predictive probability of success and the assessment of futility in large outcomes trials

We consider a class of futility rules based on a Bayesian approach for computing the predictive probability of success for large clinical trials, given a certain amount of observed data. This paper focuses on outcomes trials in particular, thus we are concerned with binary response variables. The proposed method determines the likelihood of observing a statistically significant treatment effect at the end of a study, conditional on the data observed at an interim time point and assuming that event rates governing future observations follow beta distributions. In particular, the prior distributions for the event rates of interest are updated based on the observed data at an interim time point, such that means and variances are intuitive functions of the data. Computational aspects will be discussed for the case in which event counts are functions of sample size and event rates only, and for situations in which they are functions of sample size, event rates, and exposure duration. We will discuss appropriate thresholds for declaring futility based on this approach, and the potential impact of overdispersion, a common phenomenon particularly in global outcomes trials.