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

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