Interim Monitoring of Sequential Multiple Assignment Randomized Trials Using Partial Information

Sequential multiple assignment randomized trials (SMARTs) are the gold standard trial design to generate data for the evaluation of multi-stage treatment regimes. As with conventional (single-stage) randomized clinical trials, interim monitoring allows early stopping; however, there are few methods for principled interim analysis in SMARTs. Because SMARTs involve multiple stages of treatment, a key challenge is that not all enrolled participants will have progressed through all treatment stages at the time of an interim analysis. Wu et al. (2021) propose an estimator for the mean outcome under a given regime that uses data only from participants who have completed all treatment stages. We propose a doubly-robust estimator for the mean outcome under a given regime that gains efficiency by using partial information from enrolled participants regardless of their progression through treatment stages. Using the asymptotic distribution of this estimator, we derive associated Pocock and O'Brien-Fleming testing procedures for early stopping. In simulation experiments, the estimator controls type I error and achieves nominal power while reducing expected sample size relative to the method of Wu et al. (2021). We provide an illustrative application of the proposed estimator using a case study based on a recent SMART evaluating behavioral pain interventions for breast cancer patients.