Stratification and weighting via the propensity score in estimation of causal treatment effects: a comparative study
Estimation of treatment effects with causal interpretation from observational data is complicated because exposure to treatment may be confounded with subject characteristics. The propensity score, the probability of treatment exposure conditional on covariates, is the basis for two approaches to ad...
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Published in | Statistics in medicine Vol. 23; no. 19; pp. 2937 - 2960 |
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Main Authors | , |
Format | Journal Article |
Language | English |
Published |
Chichester, UK
John Wiley & Sons, Ltd
15.10.2004
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Subjects | |
Online Access | Get full text |
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Summary: | Estimation of treatment effects with causal interpretation from observational data is complicated because exposure to treatment may be confounded with subject characteristics. The propensity score, the probability of treatment exposure conditional on covariates, is the basis for two approaches to adjusting for confounding: methods based on stratification of observations by quantiles of estimated propensity scores and methods based on weighting observations by the inverse of estimated propensity scores. We review popular versions of these approaches and related methods offering improved precision, describe theoretical properties and highlight their implications for practice, and present extensive comparisons of performance that provide guidance for practical use. Copyright © 2004 John Wiley & Sons, Ltd. |
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Bibliography: | ark:/67375/WNG-GZ6WN49P-M istex:69915013E3EFF19D7B195B1ACE55AE70E70F7DF5 ArticleID:SIM1903 NIH - No. R01-CA085848; No. R37-AI031789 ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ISSN: | 0277-6715 1097-0258 |
DOI: | 10.1002/sim.1903 |