Sample-targeted clinical trial adaptation

TL;DR

This paper introduces a novel sample size adjustment method for clinical trial adaptation, leveraging auxiliary data and stratification to improve trial efficiency while minimizing outcome distortion.

Contribution

It proposes a new approach focusing on sample size adjustment using auxiliary data and stratification, enhancing trial adaptation methods.

Findings

Effective in simulated data experiments

Reduces trial costs and duration

Maintains outcome integrity

Abstract

Clinical trial adaptation refers to any adjustment of the trial protocol after the onset of the trial. The main goal is to make the process of introducing new medical interventions to patients more efficient by reducing the cost and the time associated with evaluating their safety and efficacy. The principal question is how should adaptation be performed so as to minimize the chance of distorting the outcome of the trial. We propose a novel method for achieving this. Unlike previous work our approach focuses on trial adaptation by sample size adjustment. We adopt a recently proposed stratification framework based on collected auxiliary data and show that this information together with the primary measured variables can be used to make a probabilistically informed choice of the particular sub-group a sample should be removed from. Experiments on simulated data are used to illustrate the effectiveness of our method and its application in practice.