  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.
