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Dynamic treatment regimens (DTRs), also known as treatment algorithms oradaptive interventions, play an increasingly important role in many healthdomains. DTRs are motivated to address the unique and changing needs ofindividuals by delivering the type of treatment needed, when needed, whileminimizing unnecessary treatment. Practically, a DTR is a sequence of decisionrules that specify, for each of several points in time, how availableinformation about the individual's status and progress should be used inpractice to decide which treatment (e.g., type or intensity) to deliver. Thesequential multiple assignment randomized trial (SMART) is an experimentaldesign widely used to empirically inform the development of DTRs. Sample sizeplanning resources for SMARTs have been developed for continuous, binary, andsurvival outcomes. However, an important gap exists in sample size estimationmethodology for SMARTs with longitudinal count outcomes. Further, in manyhealth domains, count data are overdispersed - having variance greater thantheir mean. We propose a Monte Carlo-based approach to sample size estimationapplicable to many types of longitudinal outcomes and provide a case study withlongitudinal overdispersed count outcomes. A SMART for engaging alcohol andcocaine-dependent patients in treatment is used as motivation.

Sample size estimation for comparing dynamic treatment regimens in a  SMART: a Monte Carlo-based approach and case study with longitudinal  overdispersed count outcomes