Optimal Design of Experiments for Delayed Responses in Clinical Trials

Abstract

The efficiency of optimal experimental designs when the primary endpoint is immediate is well documented. Often, however, in the practice of clinical trials there will be a delay in the response of clinical interest. Since few patients will have experienced the endpoint in the early stages of a trial, there may be little information that can be used in making a decision to modify the trial’s course. But almost always, the clinical efficacy endpoint will be measured at early time points and these measurements might be correlated with, and predictive for, the primary long-term endpoint. The focus of the definitive analysis is still the primary clinical endpoint and not these short-term endpoints. The latter may be used just as an estimate of potential treatment effect and can enhance the interim decision of dropping a treatment arm or changing the treatment allocation. The research questions are: what is the optimal number of measurements per patient and what are the optimal time intervals between these measurements? A major benefit of modeling relationships between early and late endpoints is that it makes for stronger interim assessments of long-term endpoints and therefore improves the efficiency of adaptive designs.