The lay abstract featured today (for A class of computational methods to reduce selection bias when designing Phase 3 clinical trials by Tianyu Zhan) is from Statistics in Medicine with the full article now available to read here.
When designing confirmatory Phase 3 studies, one usually evaluates one or more efficacious and safe treatment option(s) based on data from previous studies. However, several retrospective research articles reported the phenomenon of “diminished treatment effect in Phase 3” based on many case studies. Even under basic assumptions, it was shown that the commonly used estimator could substantially overestimate the efficacy of selected group(s). As alternatives, a class of computational methods was proposed to reduce estimation bias and mean squared error (MSE) with a broader scope of multiple treatment groups and flexibility to accommodate summary results by group as input. Based on simulation studies and a real data example, practical implementation guidance was provided for this class of methods under different scenarios. For more complicated problems, this framework can serve as a starting point with additional layers built in. Proposed methods can also be widely applied to other selection problems.