Design and Inference Methods for Randomized Clinical Trials

Traditionally, phase II trials have employed single-arm designs, recruiting patients exclusively for the experimental therapy, and comparing results with historical controls. Due to the limited sample size and patient heterogeneity, the characteristics of patients in new phase II trials often differ from those in the selected historical controls, leading to potential false positive or false negative conclusions. Randomized phase II trials offer a solution by randomizing patients between an experimental arm and a control arm. In this dissertation, we seek efficient designs for multi-stage randomized clinical trials and develop inference methods for the widely used odds ratio parameter. We propose a two-stage randomized phase II trial design based on Fisher's exact tests. This design includes options for early stopping due to either superiority or futility, aimed at optimizing patient enrollment whether the experimental therapy proves efficacious or not. Furthermore, we introduce a novel criterion, the weighted expected sample size, to define optimal designs for multi-stage clinical trials. We have also developed a Java software tool capable of identifying these optimal designs. Additionally, we present a bias-corrected estimator and an exact conditional confidence interval for the odds ratio in multi-stage randomized clinical trials.