ABSTRACT For the pharmaceutical industry, the main utility of futility rules is to allow early stopping of a trial when it seems unlikely to achieve its primary efficacy objectives, and it is mainly motivated by financial and ethical considerations. After a brief overview of available approaches in setting a futility rule, I will illustrate, using a case study, different rules based on conditional power, predictive probability of success, and Bayesian predictive probability of success, and will emphasize the main shortcomings that arise when using these measures, especially in sample size re‐estimation designs. I propose, as an alternative, the conditional assurance that is the probability of achieving success at the final analysis when the study was not stopped for futility. It depends on the sample size for the interim, sample size at the final analysis, and the threshold for the futility rule. But it does not need the knowledge of the observed treatment effect estimate at the interim analysis. This makes the conditional assurance very appropriate for building informative futility rules. It balances the probability of stopping for futility (when there is no treatment effect), conditional assurance, and overall power. Decision makers can better understand the levels of risk associated with stopping for futility and make informed decisions about where to spend risk based on what is acceptable to the organization.
Vladimir Dragalin (Thu,) studied this question.