Power Analysis in a SMART Design: Sample Size Estimation for Determining the Best Dynamic Treatment Regime

TL;DR

This paper develops a power analysis framework for SMART designs in precision medicine, enabling sample size estimation to identify the best dynamic treatment regime using Monte Carlo simulations.

Contribution

It introduces a novel power analysis method based on multiple comparisons with the best for SMART designs, addressing a key gap in sample size determination.

Findings

The method accurately estimates sample sizes through extensive simulations.

It effectively identifies the optimal DTR with specified power levels.

Application to a real study demonstrates practical utility.

Abstract

Sequential, multiple assignment, randomized trial (SMART) designs have become increasingly popular in the field of precision medicine by providing a means for comparing sequences of treatments tailored to the individual patient, i.e., dynamic treatment regime (DTR). The construction of evidence-based DTRs promises a replacement to adhoc one-size-fits-all decisions pervasive in patient care. However, there are substantial statistical challenges in sizing SMART designs due to the complex correlation structure between the DTRs embedded in the design. Since the primary goal of SMARTs is the construction of an optimal DTR, investigators are interested in sizing SMARTs based on the ability to screen out DTRs inferior to the optimal DTR by a given amount which cannot be done using existing methods. In this paper, we fill this gap by developing a rigorous power analysis framework that leverages multiple comparisons with the best methodology. Our method employs Monte Carlo simulation in order to compute the minimum number of individuals to enroll in an arbitrary SMART. We will evaluate our method through extensive simulation studies. We will illustrate our method by retrospectively computing the power in the Extending Treatment Effectiveness of Naltrexone SMART study.