Optimal designs for the development of personalized treatment rules

TL;DR

This paper develops optimal design strategies for multi-armed clinical trials aimed at estimating personalized treatment rules, accounting for covariates and variances, with practical applications demonstrated through a dietary trial.

Contribution

It introduces a novel optimization framework for treatment allocation in clinical trials that considers covariates and variance heterogeneity, enhancing personalized medicine.

Findings

Optimal allocation for two treatments depends only on response variances.

For three or more treatments, allocation depends on covariates and regression coefficients.

Methods are demonstrated with a real dietary clinical trial.

Abstract

We study the design of multi-armed parallel group clinical trials to estimate personalized treatment rules that identify the best treatment for a given patient with given covariates. Assuming that the outcomes in each treatment arm are given by a homoscedastic linear model, with possibly different variances between treatment arms, and that the trial subjects form a random sample from an unselected overall population, we optimize the (possibly randomized) treatment allocation allowing the allocation rates to depend on the covariates. We find that, for the case of two treatments, the approximately optimal allocation rule does not depend on the value of the covariates but only on the variances of the responses. In contrast, for the case of three treatments or more, the optimal treatment allocation does depend on the values of the covariates as well as the true regression coefficients. The methods are illustrated with a recently published dietary clinical trial.