A Data Analysis Method for Using Longitudinal Binary Outcome Data from a SMART to Compare Adaptive Interventions

Abstract

Sequential multiple assignment randomized trials (SMARTs) are a useful and increasingly popular approach for gathering information to inform the construction of adaptive interventions to treat psychological and behavioral health conditions. Until recently, analysis methods for data from SMART designs considered only a single measurement of the outcome of interest when comparing the efficacy of adaptive interventions. Lu et al. proposed a method for considering repeated outcome measurements to incorporate information about the longitudinal trajectory of change. While their proposed method can be applied to many kinds of outcome variables, they focused mainly on linear models for normally distributed outcomes. Practical guidelines and extensions are required to implement this methodology with other types of repeated outcome measures common in behavioral research. In this article, we discuss implementation of this method with repeated binary outcomes. We explain how to compare adaptive interventions in terms of various summaries of repeated binary outcome measures, including average outcome (area under the curve) and delayed effects. The method is illustrated using an empirical example from a SMART study to develop an adaptive intervention for engaging alcohol- and cocaine-dependent patients in treatment. Monte Carlo simulations are provided to demonstrate the good performance of the proposed technique.

Keywords:

• Sequential multiple randomization trial (SMART)
• longitudinal data
• binary outcome
• logistic regression

Article information

Conflict of interest disclosures: Each author signed a form for disclosure of potential conflicts of interest. No authors reported any financial or other conflicts of interest in relation to the work described.

Ethical principles: The authors affirm having followed professional ethical guidelines in preparing this work. These guidelines include obtaining informed consent from human participants, maintaining ethical treatment and respect for the rights of human or animal participants, and ensuring the privacy of participants and their data, such as ensuring that individual participants cannot be identified in reported results or from publicly available original or archival data.

Funding: This work was supported by Grant P01AA016821 from the National Institute on Alcohol Abuse and Alcoholism of the National Institutes of Health and by Grants P60 DA05186, K24 DA029062, R01 DA039901, and P50 DA039838 from the National Institute on Drug Abuse of the National Institutes of Health.

Role of the funders/sponsors: None of the funders or sponsors of this research had any role in the design and conduct of the study; collection, management, analysis, and interpretation of data; preparation, review, or approval of the manuscript; or decision to submit the manuscript for publication.

Acknowledgments: The authors would like to thank Amanda Applegate and Jessica Dolan for their assistance in preparing this article. The ideas and opinions expressed herein are those of the authors alone, and endorsement by the authors’ institutions or the National Institutes of Health is not intended and should not be inferred. The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding institutions as mentioned above. Analysis was done using R (copyright 2017 by The R Foundation for Statistical Computing) and SAS (copyright 2013 by SAS Foundation, Inc., Cary, NC, USA) software.