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A method for increasing the robustness of multiple imputation

Daniel, Rhian ORCID: and Kenward, Michael G. 2012. A method for increasing the robustness of multiple imputation. Computational Statistics & Data Analysis 56 (6) , pp. 1624-1643. 10.1016/j.csda.2011.10.006

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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.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Medicine
Subjects: R Medicine > R Medicine (General)
Uncontrolled Keywords: Doubly robust estimation; Missing data; Multiple imputation
Publisher: Elsevier
ISSN: 0167-9473
Date of Acceptance: 4 October 2011
Last Modified: 03 Nov 2022 09:48

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