Sample Size and Timepoint Tradeoffs for Comparing Dynamic Treatment Regimens in a Longitudinal SMART

• Activity Number: 185 - Novel Methods for Clinical Trial Design and Characterizing Heterogeneity
• Type: Contributed
• Date/Time: Tuesday, August 4, 2020 : 10:00 AM to 2:00 PM
• Sponsor: Mental Health Statistics Section
• Author(s): Nicholas Seewald* and Daniel Almirall
• Companies: University of Michigan and University of Michigan
• Keywords: Dynamic treatment regimes; Randomized trials; Adaptive interventions

Abstract

Clinical practice often involves delivering a sequence of treatments which adapts to a patient’s changing needs. A dynamic treatment regimen (DTR) is a sequence of pre-specified decision rules which, based on a patient’s ongoing data, recommend interventions at multiple stages of treatment. The sequential, multiple-assignment randomized trial (SMART) is a tool which can be used in the development of a high-quality DTR. Often, SMARTs involve longitudinal outcomes collected over the course of the trial. An important consideration in the design of a longitudinal-outcome SMART, as with any trial, is both the sample size and number of measurement occasions. We extend previous work which developed easy-to-use sample size formulae for common SMART designs with three timepoints in which the primary aim is to compare, at end-of-study, two embedded DTRs which recommend different first-stage treatments. We discuss practical and statistical considerations in choosing between adding individuals or measurement occasions, while respecting the unique features of a SMART, including modeling constraints and over/under-representation of sequences of treatment among participants.