A multiple imputation strategy for sequential multiple assignment randomized trials. (11th June 2014)

• Record Type: Journal Article
• Authors: Shortreed, Susan M.; Laber, Eric; Scott Stroup, T.; Pineau, Joelle; Murphy, Susan A.
• Is Part Of: Statistics in medicine. Volume 33:Number 24(2014)
• Page Start: 4202
• Page End: 4214
• Publication Date: 2014-06-11
• Subjects: Medical statistics -- Periodicals; Statistique médicale -- Périodiques; Statistiques médicales -- Périodiques; 610.727
• DOI: 10.1002/sim.6223
• Languages: English
• ISSNs: 0277-6715

Abstract

<abstract abstract-type="main" id="sim6223-abs-0001"> <title>Abstract</title> <p id="sim6223-para-0001">Sequential multiple assignment randomized trials (SMARTs) are increasingly being used to inform clinical and intervention science. In a SMART, each patient is repeatedly randomized over time. Each randomization occurs at a critical decision point in the treatment course. These critical decision points often correspond to milestones in the disease process or other changes in a patient's health status. Thus, the timing and number of randomizations may vary across patients and depend on evolving patient‐specific information. This presents unique challenges when analyzing data from a SMART in the presence of missing data. This paper presents the first comprehensive discussion of missing data issues typical of SMART studies: we describe five specific challenges and propose a flexible imputation strategy to facilitate valid statistical estimation and inference using incomplete data from a SMART. To illustrate these contributions, we consider data from the Clinical Antipsychotic Trial of Intervention and Effectiveness, one of the most well‐known SMARTs to date. Copyright © 2014 John Wiley &amp; Sons, Ltd.</p> </abstract>