Blake, Helen A; Leyrat, Clémence; Mansfield, Kathryn E; Tomlinson, Laurie A; Carpenter, James; Williamson, Elizabeth J; (2020) Estimating treatment effects with partially observed covariates using outcome regression with missing indicators. Biometrical Journal, 62 (2). pp. 428-443. ISSN 0323-3847 DOI: https://doi.org/10.1002/bimj.201900041
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Abstract
Missing data is a common issue in research using observational studies to investigate the effect of treatments on health outcomes. When missingness occurs only in the covariates, a simple approach is to use missing indicators to handle the partially observed covariates. The missing indicator approach has been criticized for giving biased results in outcome regression. However, recent papers have suggested that the missing indicator approach can provide unbiased results in propensity score analysis under certain assumptions. We consider assumptions under which the missing indicator approach can provide valid inferences, namely, (1) no unmeasured confounding within missingness patterns; either (2a) covariate values of patients with missing data were conditionally independent of treatment or (2b) these values were conditionally independent of outcome; and (3) the outcome model is correctly specified: specifically, the true outcome model does not include interactions between missing indicators and fully observed covariates. We prove that, under the assumptions above, the missing indicator approach with outcome regression can provide unbiased estimates of the average treatment effect. We use a simulation study to investigate the extent of bias in estimates of the treatment effect when the assumptions are violated and we illustrate our findings using data from electronic health records. In conclusion, the missing indicator approach can provide valid inferences for outcome regression, but the plausibility of its assumptions must first be considered carefully.
Item Type | Article |
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Faculty and Department |
Faculty of Public Health and Policy > Dept of Health Services Research and Policy Faculty of Epidemiology and Population Health > Dept of Medical Statistics Faculty of Epidemiology and Population Health > Dept of Non-Communicable Disease Epidemiology |
Research Centre | Centre for Data and Statistical Science for Health (DASH) |
PubMed ID | 31994239 |
Elements ID | 141775 |
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