A multiple imputation strategy for sequential multiple assignment randomized trials.

TitleA multiple imputation strategy for sequential multiple assignment randomized trials.
Publication TypeJournal Article
Year of Publication2014
AuthorsShortreed, Susan M., Eric Laber, Scott T Stroup, Joelle Pineau, and Susan A. Murphy
JournalStat Med
Volume33
Issue24
Pagination4202-14
Date Published2014 Oct 30
ISSN1097-0258
KeywordsAntipsychotic Agents, Data Interpretation, Statistical, Decision Making, Humans, Longitudinal Studies, Randomized Controlled Trials as Topic, Regression Analysis, Schizophrenia
Abstract

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.

DOI10.1002/sim.6223
Alternate JournalStat Med
Original PublicationA multiple imputation strategy for sequential multiple assignment randomized trials.
PubMed ID24919867
PubMed Central IDPMC4184954
Grant ListN01MH90001 / MH / NIMH NIH HHS / United States
P50 DA010075 / DA / NIDA NIH HHS / United States
U54 EB020404 / EB / NIBIB NIH HHS / United States
/ CAPMC / CIHR / Canada
P50 DA10075 / DA / NIDA NIH HHS / United States
P01 CA142538 / CA / NCI NIH HHS / United States
R01 MH080015 / MH / NIMH NIH HHS / United States
Project: