The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.
A comparison of propensity score methods: a case‐study estimating the effectiveness of post‐AMI statin use
Abstract
There is an increasing interest in the use of propensity score methods to estimate causal effects in observational studies. However, recent systematic reviews have demonstrated that propensity score methods are inconsistently used and frequently poorly applied in the medical literature. In this study, we compared the following propensity score methods for estimating the reduction in all‐cause mortality due to statin therapy for patients hospitalized with acute myocardial infarction: propensity‐score matching, stratification using the propensity score, covariate adjustment using the propensity score, and weighting using the propensity score. We used propensity score methods to estimate both adjusted treated effects and the absolute and relative risk reduction in all‐cause mortality. We also examined the use of statistical hypothesis testing, standardized differences, box plots, non‐parametric density estimates, and quantile–quantile plots to assess residual confounding that remained after stratification or matching on the propensity score. Estimates of the absolute reduction in 3‐year mortality ranged from 2.1 to 4.5 per cent, while estimates of the relative risk reduction ranged from 13.3 to 17.0 per cent. Adjusted estimates of the reduction in the odds of 3‐year death varied from 15 to 24 per cent across the different propensity score methods. Copyright © 2005 John Wiley & Sons, Ltd.
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Cites
An Introduction to the Bootstrap
Efron, B., & Tibshirani, R. J. (1994) · doi:10.1201/9780429246593 · Cited by 40,500
The central role of the propensity score in observational studies for causal effects
ROSENBAUM, P. R., & RUBIN, D. B. (1983) · Biometrika · doi:10.1093/biomet/70.1.41 · Cited by 31,241
An Introduction to the Bootstrap
Efron, B., & Tibshirani, R. J. (1993) · doi:10.1007/978-1-4899-4541-9 · Cited by 21,230
Harrell, F. E. (2001) · Springer Series in Statistics · doi:10.1007/978-1-4757-3462-1 · Cited by 9,195
Harrell (2015) · Springer Series in Statistics · doi:10.1007/978-3-319-19425-7 · Cited by 6,878
Reducing Bias in Observational Studies Using Subclassification on the Propensity Score
Rosenbaum, P. R., & Rubin, D. B. (1984) · Journal of the American Statistical Association · doi:10.1080/01621459.1984.10478078 · Cited by 3,082
Estimating Causal Effects from Large Data Sets Using Propensity Scores
Rubin, D. B. (1997) · Annals of Internal Medicine · doi:10.7326/0003-4819-127-8_part_2-199710151-00064 · Cited by 2,877
Estimation of Regression Coefficients When Some Regressors are not Always Observed
Robins, J. M., Rotnitzky, A., & Zhao, L. P. (1994) · Journal of the American Statistical Association · doi:10.1080/01621459.1994.10476818 · Cited by 2,823
Using Propensity Scores to Help Design Observational Studies: Application to the Tobacco Litigation
Rubin, D. B. (2001) · Health Services and Outcomes Research Methodology · doi:10.1023/a:1020363010465 · Cited by 2,228
Lunceford, J. K., & Davidian, M. (2004) · Statistics in Medicine · doi:10.1002/sim.1903 · Cited by 1,674
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Stephenson, A. L., Ramos, K. J., Sykes, J., Ma, X., Stanojevic, S., Quon, B. S., Marshall, B. C., Petren, K., Ostrenga, J. S., Fink, A. K., et al. (2021) · The Journal of Heart and Lung Transplantation · doi:10.1016/j.healun.2020.12.001 · Cited by 30
Metadata
- Authors
- Austin, P. C., & Mamdani, M. M.
- Venue
- Statistics in Medicine
- Publisher
- Wiley
- Published
- 2005-10-11
- Volume
- 25
- Issue
- 12
- Pages
- 2084-2106
- ISSN
- 0277-6715 (ISSN-L)
- 1097-0258
- OpenAlex
- W2102520493
- Cited by
- 560
- References
- 31
- FWCI
- 13.73
- Type
- journal article
- Language
- en
- Open access
- closed
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