Causal Inference with Missingness in Confounders
Date
2019-08-15
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Abstract
Causal inference is the process of uncovering causal connection between the effect variable and disease outcome in epidemiologic research. Confounders that influence both the effect variable and outcome need to be accounted for when obtaining the causal effect in observational studies. In addition, missing data often arise in the data collection procedure, working with complete cases often results in biased estimates. We consider the estimation of causal effect in the presence of missingness in the confounders under the missing at random assumption. We investigate how different estimators namely regression, G-estimation, propensity score-based estimators including matching, stratification, weighting, propensity regression and finally doubly robust estimator, perform when applying complete-case analysis or multiple imputation. Due to the uncertainty of imputation model and computational challenge for large number of imputations, we propose an expectation-maximization (EM) algorithm to estimate the expected values of the missing confounder and utilize weighting approach in the estimation of average treatment effect. Simulation studies are conducted to see whether there is any gain in estimation efficiency under the proposed method than complete case analysis and multiple imputation. The analysis identified EM method as most efficient and accurate method for dealing missingness in confounder except for propensity score matching and inverse weighting estimators. In these two estimators, multiple imputation is found as efficient, however EM is efficient for inverse weighting when the outcome is binary. Real life data application is shown for estimating the effect of adjuvant radiation treatment on patient's survival status after 10 years of breast cancer diagnosis. Under missing completely at random (MCAR) mechanism, EM is found as the most accurate method for handling missingness in confounder than multiple imputation.
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Bagmar, M. S. H. (2019). Causal Inference with Missingness in Confounders (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.