A multiple imputation strategy for sequential multiple assignment randomized trials

Stat Med. 2014 Oct 30;33(24):4202-14. doi: 10.1002/sim.6223. Epub 2014 Jun 11.


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.

Trial registration: ClinicalTrials.gov NCT00140001.

Keywords: dynamic treatment regimes; individualized treatment; missing data; multiple imputation; sequential multiple assignment randomized trials; treatment policies.

Publication types

  • Randomized Controlled Trial
  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Antipsychotic Agents / therapeutic use
  • Data Interpretation, Statistical*
  • Decision Making
  • Humans
  • Longitudinal Studies
  • Randomized Controlled Trials as Topic / methods*
  • Regression Analysis
  • Schizophrenia / drug therapy


  • Antipsychotic Agents

Associated data

  • ClinicalTrials.gov/NCT00140001