Binary Variable Multiple Imputation with Multiple Models to Address Missing Data Mechanism Uncertainty: Application to a Smoking Cessation Trial

• Activity Number: 159
• Type: Topic Contributed
• Date/Time: Monday, July 30, 2012 : 10:30 AM to 12:20 PM
• Sponsor: Health Policy Statistics Section
• Author(s): Juned Siddique*+ and Ofer Harel, Ph.D. and Catherine Mary Crespi and Donald Hedeker
• Companies: Northwestern University and University of Connecticut and University of California at Los Angeles and University of Illinois at Chicago
• Keywords: nonignorable ; NMAR ; MNAR ; not missing at random ; missing not at random ; dropout

Abstract

We present a method for generating multiple imputations for binary variables when the missing data mechanism is unknown. Imputations are generated from more than one imputation model in order to incorporate uncertainty regarding the missing data mechanism. Parameter estimates based on the different imputation models are combined using the rules of nested multiple imputation. Through the use of simulation, we investigate the impact of missing data mechanism uncertainty on post-imputation inferences and show that incorporating this uncertainty can increase the coverage of parameter estimates. We apply our method to a longitudinal smoking cessation trial where nonignorably missing data were a concern. We show that different assumptions regarding the missing data mechanism can have a substantial impact on subsequent inferences. Our method provides a simple approach for formalizing subjective notions regarding nonresponse so that they can be easily stated, communicated, and compared.