Multi-objective optimal designs in comparative clinical trials with covariates: The reinforced doubly adaptive biased coin design

TL;DR

This paper introduces a new adaptive design for clinical trials that optimally balances ethical considerations, statistical precision, and randomness by adjusting treatment allocations based on covariates and unknown parameters.

Contribution

It proposes the reinforced doubly adaptive biased coin design, a flexible covariate-adjusted response-adaptive method for optimal treatment allocation in clinical trials.

Findings

The design effectively balances ethical and inferential goals.

Theoretical properties are established and validated through simulations.

The method adapts to covariate distributions and unknown parameters.

Abstract

The present paper deals with the problem of allocating patients to two competing treatments in the presence of covariates or prognostic factors in order to achieve a good trade-off among ethical concerns, inferential precision and randomness in the treatment allocations. In particular we suggest a multipurpose design methodology that combines efficiency and ethical gain when the linear homoscedastic model with both treatment/covariate interactions and interactions among covariates is adopted. The ensuing compound optimal allocations of the treatments depend on the covariates and their distribution on the population of interest, as well as on the unknown parameters of the model. Therefore, we introduce the reinforced doubly adaptive biased coin design, namely a general class of covariate-adjusted response-adaptive procedures that includes both continuous and discontinuous randomization functions, aimed to target any desired allocation proportion. The properties of this proposal are described both theoretically and through simulations.