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December 2012 Addressing missing data mechanism uncertainty using multiple-model multiple imputation: Application to a longitudinal clinical trial
Juned Siddique, Ofer Harel, Catherine M. Crespi
Ann. Appl. Stat. 6(4): 1814-1837 (December 2012). DOI: 10.1214/12-AOAS555

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.

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Juned Siddique. Ofer Harel. Catherine M. Crespi. "Addressing missing data mechanism uncertainty using multiple-model multiple imputation: Application to a longitudinal clinical trial." Ann. Appl. Stat. 6 (4) 1814 - 1837, December 2012. https://doi.org/10.1214/12-AOAS555

Information

Published: December 2012
First available in Project Euclid: 27 December 2012

zbMATH: 1257.62113
MathSciNet: MR3058691
Digital Object Identifier: 10.1214/12-AOAS555

Keywords: missing not at random , MNAR , NMAR , nonignorable , not missing at random

Rights: Copyright © 2012 Institute of Mathematical Statistics

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Vol.6 • No. 4 • December 2012
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