Estimation of treatment effects following a sequential trial of multiple treatments

TL;DR

This paper develops a method for accurately estimating treatment effects in complex sequential clinical trials with multiple treatments, using Rao-Blackwellisation and reverse simulations to improve inference after interim analyses.

Contribution

It introduces a novel approach combining Rao-Blackwellisation and reverse simulations for final analysis in adaptive multi-treatment trials, addressing complexities beyond single-treatment comparisons.

Findings

Enhanced unbiased treatment effect estimates via Rao-Blackwellisation.

Approximate confidence intervals for treatment differences.

Method applicable to complex adaptive trial designs.

Abstract

When a clinical trial is subject to a series of interim analyses as a result of which the study may be terminated or modified, final frequentist analyses need to take account of the design used. Failure to do so may result in overstated levels of significance, biased effect estimates and confidence intervals with inadequate coverage probabilities. A wide variety of valid methods of frequentist analysis have been devised for sequential designs comparing a single experimental treatment with a single control treatment. It is less clear how to perform the final analysis of a sequential or adaptive design applied in a more complex setting, for example to determine which treatment or set of treatments amongst several candidates should be recommended. This paper has been motivated by consideration of a trial in which four treatments for sepsis are to be compared, with interim analyses allowing the dropping of treatments or termination of the trial to declare a single winner or to conclude that there is little difference between the treatments that remain. The approach taken is based on the method of Rao-Blackwellisation which enhances the accuracy of unbiased estimates available from the first interim analysis by taking their conditional expectations given final sufficient statistics. Analytic approaches to determine such expectations are difficult and specific to the details of the design, and instead "reverse simulations" are conducted to construct replicate realisations of the first interim analysis from the final test statistics. The method also provides approximate confidence intervals for the differences between treatments.