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dc.contributor.authorMcIsaac, Michael A.
dc.contributor.authorCook, Richard J. 17:38:52 (GMT) 17:38:52 (GMT)
dc.descriptionThe final, definitive version of this paper has been published in Statistical Methods in Medical Research (2017), 26(1): 248–267 DOI: Published by SAGE Publishing, All rights reserved.en
dc.description.abstractInverse probability weighted estimating equations and multiple imputation are two of the most studied frameworks for dealing with incomplete data in clinical and epidemiological research. We examine the limiting behaviour of estimators arising from inverse probability weighted estimating equations, augmented inverse probability weighted estimating equations and multiple imputation when the requisite auxiliary models are misspecified. We compute limiting values for settings involving binary responses and covariates and illustrate the effects of model misspecification using simulations based on data from a breast cancer clinical trial. We demonstrate that, even when both auxiliary models are misspecified, the asymptotic biases of double-robust augmented inverse probability weighted estimators are often smaller than the asymptotic biases of estimators arising from complete-case analyses, inverse probability weighting or multiple imputation. We further demonstrate that use of inverse probability weighting or multiple imputation with slightly misspecified auxiliary models can actually result in greater asymptotic bias than the use of naïve, complete case analyses. These asymptotic results are shown to be consistent with empirical results from simulation studies.en
dc.description.sponsorshipAlexander Graham Bell Canada Graduate Scholarship, NSERC NSERC (RGPIN 155849) Canadian Institutes of Health Research (FRN 13887)en
dc.publisherSage Publishingen
dc.subjectasymptotic biasen
dc.subjectasymptotic varianceen
dc.subjectaugmented inverse probability weightingen
dc.subjectdouble robusten
dc.subjectincomplete dataen
dc.subjectinverse probability weightingen
dc.subjectmodel misspecificationen
dc.subjectmultiple imputationen
dc.titleStatistical methods for incomplete data: Some results on model misspecificationen
dcterms.bibliographicCitationMcIsaac, M., & Cook, R. J. (2017). Statistical methods for incomplete data: Some results on model misspecification. Statistical Methods in Medical Research, 26(1), 248–267.
uws.contributor.affiliation1Faculty of Mathematicsen
uws.contributor.affiliation2Statistics and Actuarial Scienceen

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