A method for increasing the robustness of multiple imputation


Daniel, RM; Kenward, MG; (2012) A method for increasing the robustness of multiple imputation. Computational statistics & data analysis, 56 (6). pp. 1624-1643. ISSN 0167-9473 DOI: https://doi.org/10.1016/j.csda.2011.10.006

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Abstract

Missing data are common wherever statistical methods are applied in practice. They present a problem in that they require that additional assumptions be made about the mechanism leading to the incompleteness of the data. By incorporating two models for the missing data process, doubly robust (DR) weighting-based methods offer some protection against misspecification bias since inferences are valid when at least one of the two models is correctly specified. The balance between robustness, efficiency and analytical complexity is one which is difficult to strike, resulting in a split between the likelihood and multiple imputation (MI) school on one hand and the weighting and DR school on the other. An extension of MI is proposed that, in certain settings, can be shown to give rise to DR estimators. It is conjectured that this additional robustness holds more generally, as demonstrated using simulation studies. The method is applied to data from the RECORD study, a clinical trial comparing anti-glycaemic combination therapies in type II diabetes patients. (C) 2011 Elsevier B.V. All rights reserved.

Item Type: Article
Keywords: Doubly robust estimation, Missing data, Multiple imputation, fully conditional specification, missing data, nonresponse models, regression-models, repeated outcomes, inference
Faculty and Department: Faculty of Epidemiology and Population Health > Dept of Medical Statistics
Research Centre: Centre for Statistical Methodology
Web of Science ID: 302033200023
URI: http://researchonline.lshtm.ac.uk/id/eprint/38944

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