Journal article
Weight trimming and propensity score weighting
PloS one, v 6(3), pp e18174-e18174
31 Mar 2011
PMID: 21483818
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Propensity score weighting is sensitive to model misspecification and outlying weights that can unduly influence results. The authors investigated whether trimming large weights downward can improve the performance of propensity score weighting and whether the benefits of trimming differ by propensity score estimation method. In a simulation study, the authors examined the performance of weight trimming following logistic regression, classification and regression trees (CART), boosted CART, and random forests to estimate propensity score weights. Results indicate that although misspecified logistic regression propensity score models yield increased bias and standard errors, weight trimming following logistic regression can improve the accuracy and precision of final parameter estimates. In contrast, weight trimming did not improve the performance of boosted CART and random forests. The performance of boosted CART and random forests without weight trimming was similar to the best performance obtainable by weight trimmed logistic regression estimated propensity scores. While trimming may be used to optimize propensity score weights estimated using logistic regression, the optimal level of trimming is difficult to determine. These results indicate that although trimming can improve inferences in some settings, in order to consistently improve the performance of propensity score weighting, analysts should focus on the procedures leading to the generation of weights (i.e., proper specification of the propensity score model) rather than relying on ad-hoc methods such as weight trimming.
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Details
- Title
- Weight trimming and propensity score weighting
- Creators
- Brian K Lee - Department of Epidemiology and Biostatistics, Drexel University School of Public Health, Philadelphia, Pennsylvania, United States of America. bklee@drexel.eduJustin LesslerElizabeth A Stuart
- Publication Details
- PloS one, v 6(3), pp e18174-e18174
- Publisher
- Public LIbrary of Science (PLOS); United States
- Grant note
- K25MH083846 / NIMH NIH HHS K25 MH083846 / NIMH NIH HHS K25 MH083846-03 / NIMH NIH HHS
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Epidemiology and Biostatistics
- Web of Science ID
- WOS:000289057200038
- Scopus ID
- 2-s2.0-79953304087
- Other Identifier
- 991014877710204721
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Statistics & Probability