Title: Propensity score estimation using classification and regression trees in the presence of missing covariate data,
Abstract: Data mining and machine learning techniques such as classification and
regression trees (CART) represent a promising alternative to conventional
logistic regression for propensity score estimation. Whereas incomplete data
preclude the fitting of a logistic regression on all subjects, CART is
appealing in part because some implementations allow for incomplete records to
be incorporated in the tree fitting and provide propensity score estimates for
all subjects. Based on theoretical considerations, we argue that the automatic
handling of missing data by CART may however not be appropriate. Using a series
of simulation experiments, we examined the performance of different approaches
to handling missing covariate data; (i) applying the CART algorithm directly to
the (partially) incomplete data, (ii) complete case analysis, and (iii)
multiple imputation. Performance was assessed in terms of bias in estimating
exposure-outcome effects \add{among the exposed}, standard error, mean squared
error and coverage. Applying the CART algorithm directly to incomplete data
resulted in bias, even in scenarios where data were missing completely at
random. Overall, multiple imputation followed by CART resulted in the best
performance. Our study showed that automatic handling of missing data in CART
can cause serious bias and does not outperform multiple imputation as a means
to account for missing data.