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An Empirical Comparison of Tree‐Based Methods for Propensity Score Estimation
Author(s) -
Watkins Stephanie,
JonssonFunk Michele,
Brookhart M. Alan,
Rosenberg Steven A.,
O'Shea T. Michael,
Daniels Julie
Publication year - 2013
Publication title -
health services research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.706
H-Index - 121
eISSN - 1475-6773
pISSN - 0017-9124
DOI - 10.1111/1475-6773.12068
Subject(s) - covariate , propensity score matching , statistics , logistic regression , context (archaeology) , confounding , estimation , observational study , regression , mathematics , medicine , geography , management , archaeology , economics
Objective To illustrate the use of ensemble tree‐based methods (random forest classification [ RFC ] and bagging) for propensity score estimation and to compare these methods with logistic regression, in the context of evaluating the effect of physical and occupational therapy on preschool motor ability among very low birth weight ( VLBW ) children. Data Source We used secondary data from the E arly C hildhood L ongitudinal S tudy B irth C ohort ( ECLS ‐B) between 2001 and 2006. Study Design We estimated the predicted probability of treatment using tree‐based methods and logistic regression ( LR ). We then modeled the exposure‐outcome relation using weighted LR models while considering covariate balance and precision for each propensity score estimation method. Principal Findings Among approximately 500 VLBW children, therapy receipt was associated with moderately improved preschool motor ability. Overall, ensemble methods produced the best covariate balance (Mean Squared Difference: 0.03–0.07) and the most precise effect estimates compared to LR (Mean Squared Difference: 0.11). The overall magnitude of the effect estimates was similar between RFC and LR estimation methods. Conclusion Propensity score estimation using RFC and bagging produced better covariate balance with increased precision compared to LR . Ensemble methods are a useful alterative to logistic regression to control confounding in observational studies.