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Identifying optimal treatments for patients living with rare diseases is challenging due to the small numbers of individuals affected. One design used to address this challenge is known as the small n, sequential, multiple assignment, randomized trial (snSMART). To investigate the efficacy of an active drug measured by a continuous outcome tested at a low and high dose compared to placebo, we propose a new two-stage snSMART design. In stage 1, patients are randomized to an initial treatment. In stage 2, patients are re-randomized, depending on their stage 1 outcome, to either the same or a different dose of treatment. Data from both stages are used to determine the marginal efficacy of the dose levels of active treatment. We propose a Bayesian approach for borrowing information across stage 1 and stage 2 data. We compare the proposed approach to standard methods using only stage 1 data. We observe that the joint stage Bayesian method has smaller root-mean-square-error and 95% Bayesian credible interval widths than standard methods in several tested settings. We conclude that our approach using data from both stages is advantageous for efficacy inference and the proposed snSMART design is useful for the design of registration trials in rare diseases.

Comparing Dose Levels to Placebo using a Continuous Outcome in a Small n, Sequential, Multiple Assignment, Randomized trial (snSMART)