The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Conditioning on the propensity score can result in biased estimation of common measures of treatment effect: a Monte Carlo study
Abstract
Propensity score methods are increasingly being used to estimate causal treatment effects in the medical literature. Conditioning on the propensity score results in unbiased estimation of the expected difference in observed responses to two treatments. The degree to which conditioning on the propensity score introduces bias into the estimation of the conditional odds ratio or conditional hazard ratio, which are frequently used as measures of treatment effect in observational studies, has not been extensively studied. We conducted Monte Carlo simulations to determine the degree to which propensity score matching, stratification on the quintiles of the propensity score, and covariate adjustment using the propensity score result in biased estimation of conditional odds ratios, hazard ratios, and rate ratios. We found that conditioning on the propensity score resulted in biased estimation of the true conditional odds ratio and the true conditional hazard ratio. In all scenarios examined, treatment effects were biased towards the null treatment effect. However, conditioning on the propensity score did not result in biased estimation of the true conditional rate ratio. In contrast, conventional regression methods allowed unbiased estimation of the true conditional treatment effect when all variables associated with the outcome were included in the regression model. The observed bias in propensity score methods is due to the fact that regression models allow one to estimate conditional treatment effects, whereas propensity score methods allow one to estimate marginal treatment effects. In several settings with non‐linear treatment effects, marginal and conditional treatment effects do not coincide. Copyright © 2006 John Wiley & Sons, Ltd.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
The Effectiveness of Adjustment by Subclassification in Removing Bias in Observational Studies
Cochran, W. G. (1968) · Biometrics · doi:10.2307/2528036 · Cited by 961
Confounding and Collapsibility in Causal Inference
Greenland, S., Pearl, J., & Robins, J. M. (1999) · Statistical Science · doi:10.1214/ss/1009211805 · Cited by 933 · cited 1×
“This phenomenon is also known as non-collapsibility of the odds ratio [1], but is apparent in all non-linear regression models and generalized linear models with a link function other than the identity link (linear models) or log-link function [2].”
Background
Cited by
Austin, P. C. (2011) · Multivariate Behavioral Research · doi:10.1080/00273171.2011.568786 · Cited by 12,242
Self-citationAustin, P. C. (2011) · Pharmaceutical Statistics · doi:10.1002/pst.433 · Cited by 3,949
Self-citationAustin, P. C. (2014) · Statistics in Medicine · doi:10.1002/sim.5984 · Cited by 1,422
Self-citationThe performance of different propensity score methods for estimating marginal hazard ratios
Austin, P. C. (2013) · Statistics in Medicine · doi:10.1002/sim.5705 · Cited by 1,006
Self-citationAustin, P. C. (2009) · Biometrical Journal · doi:10.1002/bimj.200810488 · Cited by 769
Self-citationAustin, P. C. (2010) · American Journal of Epidemiology · doi:10.1093/aje/kwq224 · Cited by 673
Self-citationAustin, P. C. (2016) · Statistics in Medicine · doi:10.1002/sim.7084 · Cited by 542
Self-citationAustin, P. C. (2009) · Medical Decision Making · doi:10.1177/0272989x09341755 · Cited by 454
Self-citationAustin, P. C. (2010) · Statistics in Medicine · doi:10.1002/sim.3854 · Cited by 356
Self-citationPirracchio, R., Resche-Rigon, M., & Chevret, S. (2012) · BMC Medical Research Methodology · doi:10.1186/1471-2288-12-70 · Cited by 263
Dahabreh, I. J., Sheldrick, R. C., Paulus, J. K., Chung, M., Varvarigou, V., Jafri, H., Rassen, J. A., Trikalinos, T. A., & Kitsios, G. D. (2012) · European Heart Journal · doi:10.1093/eurheartj/ehs114 · Cited by 204
Survival After Bilateral Versus Single-Lung Transplantation for Idiopathic Pulmonary Fibrosis
Thabut, G., Christie, J. D., Ravaud, P., Castier, Y., Dauriat, G., Jebrak, G., Fournier, M., Lesèche, G., Porcher, R., & Mal, H. (2009) · Annals of Internal Medicine · doi:10.7326/0003-4819-151-11-200912010-00004 · Cited by 172
Arbogast, P. G., & Ray, W. A. (2011) · American Journal of Epidemiology · doi:10.1093/aje/kwr143 · Cited by 138
Gayat, E., Resche‐Rigon, M., Mary, J., & Porcher, R. (2012) · Pharmaceutical Statistics · doi:10.1002/pst.537 · Cited by 136
Rubin, D. B. (2010) · American Journal of Ophthalmology · doi:10.1016/j.ajo.2009.08.024 · Cited by 88
Townsend, S. R., Phillips, G. S., Duseja, R., Tefera, L., Cruikshank, D., Dickerson, R., Nguyen, H. B., Schorr, C. A., Levy, M. M., Dellinger, R. P., et al. (2022) · CHEST · doi:10.1016/j.chest.2021.07.2167 · Cited by 69
Héroux, J., Moodie, E. E. M., Strumpf, E., Coyle, N., Tousignant, P., & Diop, M. (2014) · Statistics in Medicine · doi:10.1002/sim.6020 · Cited by 22
Metadata
- Authors
- Austin, P. C., Grootendorst, P., Normand, S. T., & Anderson, G. M.
- Venue
- Statistics in Medicine
- Publisher
- Wiley
- Published
- 2006-06-16
- Volume
- 26
- Issue
- 4
- Pages
- 754-768
- ISSN
- 0277-6715 (ISSN-L)
- 1097-0258
- OpenAlex
- W2061407842
- Cited by
- 230
- References
- 2
- FWCI
- 12.20
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
