TY - JOUR

T1 - Doubly robust estimators of causal exposure effects with missing data in the outcome, exposure or a confounder

AU - Williamson, Elizabeth Jane

AU - Forbes, Andrew Benjamin

AU - Wolfe, Rory St John

PY - 2012

Y1 - 2012

N2 - 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.

AB - 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.

UR - http://onlinelibrary.wiley.com/doi/10.1002/sim.5643/pdf

U2 - 10.1002/sim.5643

DO - 10.1002/sim.5643

M3 - Article

VL - 31

SP - 4382

EP - 4400

JO - Statistics in Medicine

JF - Statistics in Medicine

SN - 0277-6715

IS - 30

ER -