Journal article
Improving propensity score weighting using machine learning
Statistics in medicine, v 29(3), pp 337-346
10 Feb 2010
PMID: 19960510
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Machine learning techniques such as classification and regression trees (CART) have been suggested as promising alternatives to logistic regression for the estimation of propensity scores. The authors examined the performance of various CART-based propensity score models using simulated data. Hypothetical studies of varying sample sizes (n=500, 1000, 2000) with a binary exposure, continuous outcome, and ten covariates were simulated under seven scenarios differing by degree of non-linear and non-additive associations between covariates and the exposure. Propensity score weights were estimated using logistic regression (all main effects), CART, pruned CART, and the ensemble methods of bagged CART, random forests, and boosted CART. Performance metrics included covariate balance, standard error, percent absolute bias, and 95% confidence interval coverage. All methods displayed generally acceptable performance under conditions of either non-linearity or non-additivity alone. However, under conditions of both moderate non-additivity and moderate non-linearity, logistic regression had subpar performance, while ensemble methods provided substantially better bias reduction and more consistent 95% CI coverage. The results suggest that ensemble methods, especially boosted CART, may be useful for propensity score weighting.
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Details
- Title
- Improving propensity score weighting using machine learning
- Creators
- Brian K Lee - Drexel University School of Public Health Department of Epidemiology and BiostatisticsJustin Lessler - Johns Hopkins Bloomberg School of Public Health Department of EpidemiologyElizabeth A Stuart - Department of Mental Health
- Publication Details
- Statistics in medicine, v 29(3), pp 337-346
- Publisher
- Wiley
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Epidemiology and Biostatistics
- Web of Science ID
- WOS:000274322000003
- Scopus ID
- 2-s2.0-74749097452
- Other Identifier
- 991014878139104721
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Mathematical & Computational Biology
- Medical Informatics
- Medicine, Research & Experimental
- Public, Environmental & Occupational Health
- Statistics & Probability