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We present a framework for generating multiple imputations for continuousdata when the missing data mechanism is unknown. Imputations are generated frommore than one imputation model in order to incorporate uncertainty regardingthe missing data mechanism. Parameter estimates based on the differentimputation models are combined using rules for nested multiple imputation.Through the use of simulation, we investigate the impact of missing datamechanism uncertainty on post-imputation inferences and show that incorporatingthis uncertainty can increase the coverage of parameter estimates. We apply ourmethod to a longitudinal clinical trial of low-income women with depressionwhere nonignorably missing data were a concern. We show that differentassumptions regarding the missing data mechanism can have a substantial impacton inferences. Our method provides a simple approach for formalizing subjectivenotions regarding nonresponse so that they can be easily stated, communicatedand compared.

Addressing missing data mechanism uncertainty using multiple-model multiple imputation: Application to a longitudinal clinical trial