Pragmatic Two-Stage Design for Clinical Trials With Treatment Selection

Clinical trials are inherently complex and become more challenging when selecting one treatment from many while also evaluating the efficacy. In this paper, we develop a pragmatic design that balances sample efficiency and practicality through a two-stage sequential approach. The first stage involves adaptive arm selection and early termination, while the second stage optimizes sample allocation. Inspired by the Kiefer-Weiss problem, the proposed method provides an efficient solution to an optimization framework that minimizes a weighted sum of the expected number of patients, probability of selecting a bad arm, and the probability of a global Type II error subject to constraints on the Family-Wise Error Rate and maximum sample size. The framework employs stochastic optimization to find the design parameters for the global minimum. We compare the performance against fixed and multi-arm multi-stage designs over a range of test conditions with varying error costs and treatment-response models. Our method improves the cost and practicality of a clinical trial for medical treatments or pharmaceutical development.