A response-adaptive multi-arm design for continuous endpoints based on a weighted information measure

TL;DR

This paper introduces a response-adaptive multi-arm trial design for continuous endpoints that balances patient allocation and statistical power using a context-dependent information measure, with applications in oncology.

Contribution

It proposes a novel adaptive design based on a weighted information measure that controls the trade-off between power and patient allocation, with robust tuning and error control strategies.

Findings

The method effectively balances allocation to the target arm and maintains power.

Simulation studies demonstrate advantages over traditional fixed designs.

Application to oncology trials shows practical feasibility and benefits.

Abstract

Multi-arm trials are gaining interest in practice given the statistical and logistical advantages they can offer. The standard approach uses a fixed allocation ratio, but there is a call for making it adaptive and skewing the allocation of patients towards better-performing arms. However, it is well-known that these approaches might suffer from lower statistical power. We present a response-adaptive design for continuous endpoints which explicitly allows to control the trade-off between the number of patients allocated to the "optimal" arm and the statistical power. Such a balance is achieved through the calibration of a tuning parameter, and we explore robust procedures to select it. The proposed criterion is based on a context-dependent information measure which gives greater weight to treatment arms with characteristics close to a pre-specified clinical target. We establish conditions under which the procedure consistently selects the target arm and derive the corresponding limiting allocation ratios. We also introduce a simulation-based hypothesis testing procedure which focuses on selecting the target arm and discuss strategies to effectively control the type-I error rate. The practical implementation of the proposed criterion and its potential advantage over currently used alternatives are illustrated in the context of early Phase IIa proof-of-concept oncology trials.