Information-adaptive clinical trials: a selective recruitment design

TL;DR

This paper introduces an adaptive clinical trial design that selectively recruits patients based on expected information gain, aiming to reduce sample size while elucidating covariate-treatment-survival relationships.

Contribution

The proposed method uses entropy-based criteria to adaptively select informative patients, improving trial efficiency and understanding of covariate effects.

Findings

Reduces the number of patients needed for reliable results

Demonstrates effectiveness with breast cancer data

Balances information gain with patient recruitment costs

Abstract

We propose a novel adaptive design for clinical trials with time-to-event outcomes and covariates (which may consist of or include biomarkers). Our method is based on the expected entropy of the posterior distribution of a proportional hazards model. The expected entropy is evaluated as a function of a patient's covariates, and the information gained due to a patient is defined as the decrease in the corresponding entropy. Candidate patients are only recruited onto the trial if they are likely to provide sufficient information. Patients with covariates that are deemed uninformative are filtered out. A special case is where all patients are recruited, and we determine the optimal treatment arm allocation. This adaptive design has the advantage of potentially elucidating the relationship between covariates, treatments, and survival probabilities using fewer patients, albeit at the cost of rejecting some candidates. We assess the performance of our adaptive design using data from the German Breast Cancer Study group and numerical simulations of a biomarker validation trial.