Generalized Likelihood Ratio Statistics and Uncertainty Adjustments in Efficient Adaptive Design of Clinical Trials

TL;DR

This paper introduces a new adaptive clinical trial design using generalized likelihood ratio statistics, ensuring error control and efficiency, with practical implementation guidance and extensive simulation comparisons.

Contribution

It presents a novel adaptive design framework based on generalized likelihood ratios for multiparameter exponential families, improving efficiency and error control in clinical trials.

Findings

Design maintains prescribed Type I error probability

Achieves asymptotic efficiency in trial planning

Outperforms existing methods in simulation studies

Abstract

A new approach to adaptive design of clinical trials is proposed in a general multiparameter exponential family setting, based on generalized likelihood ratio statistics and optimal sequential testing theory. These designs are easy to implement, maintain the prescribed Type I error probability, and are asymptotically efficient. Practical issues involved in clinical trials allowing mid-course adaptation and the large literature on this subject are discussed, and comparisons between the proposed and existing designs are presented in extensive simulation studies of their finite-sample performance, measured in terms of the expected sample size and power functions.