Journal
STATISTICAL METHODS IN MEDICAL RESEARCH
Volume 26, Issue 1, Pages 248-267Publisher
SAGE PUBLICATIONS LTD
DOI: 10.1177/0962280214544251
Keywords
asymptotic bias; asymptotic variance; augmented inverse probability weighting; double robust; incomplete data; inverse probability weighting; model misspecification; multiple imputation
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Funding
- Alexander Graham Bell Canada Graduate Scholarship from the Natural Sciences and Engineering Research Council (NSERC) of Canada
- NSERC [RGPIN 155849]
- Canadian Institutes of Health Research [FRN 13887]
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Inverse 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 naive, complete case analyses. These asymptotic results are shown to be consistent with empirical results from simulation studies.
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