In this post, I walk through steps of running propensity score analysis when there is missingness in the covariate data. Particularly, I look at multiple imputation and ways to condition on propensity scores estimated with imputed data. The code builds on my earlier post where I go over different ways to handle missing data when conducting propensity score analysis. I go through tidyeval way of dealing with multiply imputed data.

Theories behind propensity score analysis assume that the covariates are fully observed (Rosenbaum & Rubin, 1983, 1984). However, in practice, observational analyses require large administrative databases or surveys, which inevitably will have missingness in the covariates. The response patterns of people with missing covariates may be different than those of people with observed data (Mohan, Pearl, & Tian, 2013). Therefore, ways to handle missing covariate data need to be examined.

Missing data treatment using multivariate methods for two-group comparisons and small sample sizes.

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