The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
Using Inverse Probability-Weighted Estimators in Comparative Effectiveness Analyses With Observational Databases
Abstract
Inverse probability-weighted estimation is a powerful tool for use with observational data. In this article, we describe how this propensity score-based method can be used to compare the effectiveness of 2 or more treatments. First, we discuss the inherent problems in using observational data to assess comparative effectiveness. Next, we provide a conceptual explanation of inverse probability-weighted estimation and point readers to sources that address the method in more formal, technical terms. Finally, we offer detailed guidance about how to implement the estimators in comparative effectiveness analyses.
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Citations
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
Estimating causal effects of treatments in randomized and nonrandomized studies
Rubin, D. B. (1974) · Journal of Educational Psychology · doi:10.1037/h0037350 · Cited by 9,436
Statistics and Causal Inference
Holland, P. W. (1986) · Journal of the American Statistical Association · doi:10.1080/01621459.1986.10478354 · Cited by 5,293
Rosenbaum, P. R., & Rubin, D. B. (1985) · The American Statistician · doi:10.2307/2683903 · Cited by 4,774
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
Bayesian inference for causal effects: The role of randomization
Rubin, D. B. (1978) · The Annals of Statistics · doi:10.1214/aos/1176344064 · Cited by 2,503
Variable Selection for Propensity Score Models
Brookhart, M. A., Schneeweiss, S., Rothman, K. J., Glynn, R. J., Avorn, J., & Stürmer, T. (2006) · American Journal of Epidemiology · doi:10.1093/aje/kwj149 · Cited by 2,336
Hernán, M. Á., Brumback, B., & Robins, J. M. (2000) · Epidemiology · doi:10.1097/00001648-200009000-00012 · Cited by 1,782
Cited by
Mazzaglia, G., Ambrosioni, E., Alacqua, M., Filippi, A., Sessa, E., Immordino, V., Borghi, C., Brignoli, O., Caputi, A. P., Cricelli, C., et al. (2009) · Circulation · doi:10.1161/circulationaha.108.830299 · Cited by 637
Comparative Effectiveness of Revascularization Strategies
Weintraub, W. S., Grau-Sepulveda, M. V., Weiss, J. M., O'Brien, S. M., Peterson, E. D., Kolm, P., Zhang, Z., Klein, L. W., Shaw, R. E., McKay, C., et al. (2012) · New England Journal of Medicine · doi:10.1056/nejmoa1110717 · Cited by 608
Prenatal exposure to antibiotics, cesarean section and risk of childhood obesity
Mueller, N. T., Whyatt, R., Hoepner, L., Oberfield, S., Dominguez-Bello, M. G., Widen, E. M., Hassoun, A., Perera, F., & Rundle, A. (2015) · International Journal of Obesity · doi:10.1038/ijo.2014.180 · Cited by 485
Challenges of Guarantee-Time Bias
Giobbie-Hurder, A., Gelber, R. D., & Regan, M. M. (2013) · Journal of Clinical Oncology · doi:10.1200/jco.2013.49.5283 · Cited by 458
Kim, G.-A., Lim, Y.-S., Han, S., Choi, J., Shim, J. H., Kim, K. M., Lee, H. C., & Lee, Y. S. (2018) · Gut · doi:10.1136/gutjnl-2017-314904 · Cited by 258
Metadata
- Authors
- Curtis, L. H., Hammill, B. G., Eisenstein, E. L., Kramer, J. M., & Anstrom, K. J.
- Venue
- Medical Care
- Published
- 2007-10
- Volume
- 45
- Issue
- 10
- Pages
- S103-S107
- ISSN
- 0025-7079 (ISSN-L)
- OpenAlex
- W2040113464
- Cited by
- 494
- References
- 24
- FWCI
- 5.71
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Using Inverse Probability-Weighted Estimators in Comparative Effectiveness Analyses With Observational Databases. https://app.cochat.ai/papers/10.1097/mlr.0b013e31806518ac
- The paper
- Curtis, L. H., Hammill, B. G., Eisenstein, E. L., Kramer, J. M., & Anstrom, K. J. (2007). Using Inverse Probability-Weighted Estimators in Comparative Effectiveness Analyses With Observational Databases. Medical Care. https://doi.org/10.1097/mlr.0b013e31806518ac
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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