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

TL;DR

This paper introduces a multiple-model multiple imputation framework to handle uncertainty about the missing data mechanism in longitudinal clinical trials, improving inference accuracy.

Contribution

It proposes a novel method for generating multiple imputations from different models to account for missing data mechanism uncertainty.

Findings

Increased coverage of parameter estimates when incorporating mechanism uncertainty

Significant impact of missing data assumptions on clinical trial inferences

Method facilitates formalization and communication of subjective nonresponse notions

Abstract

We present a framework for generating multiple imputations for continuous data 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 rules for 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 clinical trial of low-income women with depression where nonignorably missing data were a concern. We show that different assumptions regarding the missing data mechanism can have a substantial impact on inferences. Our method provides a simple approach for formalizing subjective notions regarding nonresponse so that they can be easily stated, communicated and compared.