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Comparing dynamic treatment regimes using repeated‐measures outcomes: modeling considerations in SMART studies
Author(s) -
Lu Xi,
NahumShani Inbal,
Kasari Connie,
Lynch Kevin G.,
Oslin David W.,
Pelham William E.,
Fabiano Gregory,
Almirall Daniel
Publication year - 2015
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.6819
Subject(s) - repeated measures design , computer science , outcome (game theory) , clinical trial , autism spectrum disorder , clinical study design , machine learning , econometrics , autism , psychology , statistics , medicine , psychiatry , mathematics , mathematical economics , pathology
A dynamic treatment regime (DTR) is a sequence of decision rules, each of which recommends a treatment based on a patient's past and current health status. Sequential, multiple assignment, randomized trials (SMARTs) are multi‐stage trial designs that yield data specifically for building effective DTRs. Modeling the marginal mean trajectories of a repeated‐measures outcome arising from a SMART presents challenges, because traditional longitudinal models used for randomized clinical trials do not take into account the unique design features of SMART. We discuss modeling considerations for various forms of SMART designs, emphasizing the importance of considering the timing of repeated measures in relation to the treatment stages in a SMART. For illustration, we use data from three SMART case studies with increasing level of complexity, in autism, child attention deficit hyperactivity disorder, and adult alcoholism. In all three SMARTs, we illustrate how to accommodate the design features along with the timing of the repeated measures when comparing DTRs based on mean trajectories of the repeated‐measures outcome. Copyright © 2015 John Wiley & Sons, Ltd.