Abstract for: Trial Termination and Drug Misclassification in Sequential Adaptive Clinical Trials
Sequential adaptive clinical trials allow for early termination of drug testing for benefit or futility at interim analysis points. Early stopping allows the trial sponsor to mitigate investment risks on ineffective drugs, and to shorten the development timeline of effective drugs, hence reducing expenditures and expediting patients’ access to these new therapies. However, this new flexibility may translate into a higher drug misclassification rate (i.e., false positives and false negatives). We examine the nature and implications of wrongly terminating the development of an effective drug, which may lead to unrecoverable expenses for the sponsor, and unfulfilled patients’ needs. To this end, we build a system dynamics model of a Phase 3 sequential adaptive trial, and focus on the continuation or termination decision at an interim analysis point, based on the feedback from the drug testing process. This feedback’s accuracy depends on the sample size, and the drug’s true efficacy. We examine the effects of imperfect information and the conditions that lead to drug misclassification by conducting simulations. Contrary to the literature’s focus on false positives, our results suggest that false negatives can be more likely. We also find that misclassification rate does not necessarily decrease with large sample sizes. Lastly, we provide insights for investigators and other decision makers on the implications of false negatives.