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

TL;DR

This paper introduces a Monte Carlo-based method for estimating sample sizes in SMART trials with longitudinal overdispersed count outcomes, addressing a gap in existing methodologies.

Contribution

It develops a novel Monte Carlo approach for sample size calculation in SMARTs involving overdispersed count data, expanding applicability to diverse longitudinal outcomes.

Findings

Method effectively estimates sample sizes for overdispersed count outcomes.

Case study demonstrates practical application in addiction treatment research.

Addresses a key gap in SMART sample size planning for count data.

Abstract

Dynamic treatment regimens (DTRs), also known as treatment algorithms or adaptive interventions, play an increasingly important role in many health domains. DTRs are motivated to address the unique and changing needs of individuals by delivering the type of treatment needed, when needed, while minimizing unnecessary treatment. Practically, a DTR is a sequence of decision rules that specify, for each of several points in time, how available information about the individual's status and progress should be used in practice to decide which treatment (e.g., type or intensity) to deliver. The sequential multiple assignment randomized trial (SMART) is an experimental design widely used to empirically inform the development of DTRs. Sample size planning resources for SMARTs have been developed for continuous, binary, and survival outcomes. However, an important gap exists in sample size estimation methodology for SMARTs with longitudinal count outcomes. Further, in many health domains, count data are overdispersed - having variance greater than their mean. We propose a Monte Carlo-based approach to sample size estimation applicable to many types of longitudinal outcomes and provide a case study with longitudinal overdispersed count outcomes. A SMART for engaging alcohol and cocaine-dependent patients in treatment is used as motivation.