Efficient and Robust Approaches for Analysis of SMARTs: Illustration using the ADAPT-R Trial

TL;DR

This paper introduces a robust and efficient TMLE-based method for analyzing SMART trial data, enabling precise estimation of embedded treatment regimes with valid inference, demonstrated through simulations and the ADAPT-R HIV retention trial.

Contribution

It presents the first primary analysis application of TMLE for SMART trials, improving estimation accuracy and inference robustness for embedded adaptive treatment strategies.

Findings

TMLE provides more precise estimates than G-computation and IPW.

The method yields valid confidence intervals for embedded regimes.

Application to ADAPT-R demonstrates practical utility in HIV care strategies.

Abstract

Personalized intervention strategies, in particular those that modify treatment based on a participant's own response, are a core component of precision medicine approaches. Sequential Multiple Assignment Randomized Trials (SMARTs) are growing in popularity and are specifically designed to facilitate the evaluation of sequential adaptive strategies, in particular those embedded within the SMART. Advances in efficient estimation approaches that are able to incorporate machine learning while retaining valid inference can allow for more precise estimates of the effectiveness of these embedded regimes. However, to the best of our knowledge, such approaches have not yet been applied as the primary analysis in SMART trials. In this paper, we present a robust and efficient approach using Targeted Maximum Likelihood Estimation (TMLE) for estimating and contrasting expected outcomes under the dynamic regimes embedded in a SMART, together with generating simultaneous confidence intervals for the resulting estimates. We contrast this method with two alternatives (G-computation and Inverse Probability Weighting estimators). The precision gains and robust inference achievable through the use of TMLE to evaluate the effects of embedded regimes are illustrated using both outcome-blind simulations and a real data analysis from the Adaptive Strategies for Preventing and Treating Lapses of Retention in HIV Care (ADAPT-R) trial (NCT02338739), a SMART with a primary aim of identifying strategies to improve retention in HIV care among people living with HIV in sub-Saharan Africa.