A Multiple Imputation Strategy for Sequential Multiple Assignment Randomized Trials

• Activity Number: 320
• Type: Contributed
• Date/Time: Tuesday, August 6, 2013 : 8:30 AM to 10:20 AM
• Sponsor: Section on Statistics in Epidemiology
• Author(s): Susan Shortreed*+ and Eric Laber and Joelle Pineau and Susan Murphy
• Companies: Group Health Research Institute and NC State University and McGill University and University of Michigan
• Keywords: missing data ; sequential multiple assignment randomized trials ; dynamic treatment regimes ; multiple imputation ; 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. Here, we describe these challenges and propose an imputation strategy that facilitates valid statistical estimation and inference. To keep our development concrete, we use data from the Clinical Antipsychotic Trial of Intervention and Effectiveness, a SMART study of 1460 patients with schizophrenia, as a running example.