Doubly robust estimators of causal exposure effects with missing data in the outcome, exposure or a confounder
AuthorWilliamson, EJ; Forbes, A; Wolfe, R
Source TitleStatistics in Medicine
University of Melbourne Author/sWILLIAMSON, ELIZABETH
AffiliationMelbourne School Of Population And Global Health
Document TypeJournal Article
CitationsWilliamson, E. J., Forbes, A. & Wolfe, R. (2012). Doubly robust estimators of causal exposure effects with missing data in the outcome, exposure or a confounder. STATISTICS IN MEDICINE, 31 (30), pp.4382-4400. https://doi.org/10.1002/sim.5643.
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C1 - Journal Articles Refereed
We consider the estimation of the causal effect of a binary exposure on a continuous outcome. Confounding and missing data are both likely to occur in practice when observational data are used to estimate this causal effect. In dealing with each of these problems, model misspecification is likely to introduce bias. We present augmented inverse probability weighted (AIPW) estimators that account for both confounding and missing data, with the latter occurring in a single variable only. These estimators have an element of robustness to misspecification of the models used. Our estimators require two models to be specified to deal with confounding and two to deal with missing data. Only one of each of these models needs to be correctly specified. When either the outcome or the exposure of interest is missing, we derive explicit expressions for the AIPW estimator. When a confounder is missing, explicit derivation is complex, so we use a simple algorithm, which can be applied using standard statistical software, to obtain an approximation to the AIPW estimator.
KeywordsStatistics not elsewhere classified; Epidemiology; Expanding Knowledge in the Medical and Health Sciences; Expanding Knowledge in the Mathematical Sciences
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