The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Implications of the Propensity Score Matching Paradox in Pharmacoepidemiology
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.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“The PSM is based on the probability of assignment to the treatment group, and was selected due to a good balance on the covariates, rather than Mahalanobis distance matching, which is based on covariate values (19).”
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
Austin, P. C. (2011) · Multivariate Behavioral Research · doi:10.1080/00273171.2011.568786 · Cited by 12,242
Austin, P. C. (2009) · Statistics in Medicine · doi:10.1002/sim.3697 · Cited by 6,758
Matching Methods for Causal Inference: A Review and a Look Forward
Stuart, E. A. (2010) · Statistical Science · doi:10.1214/09-sts313 · Cited by 5,478
MatchIt : Nonparametric Preprocessing for Parametric Causal Inference
Ho, D. E., Imai, K., King, G., & Stuart, E. A. (2011) · Journal of Statistical Software · doi:10.18637/jss.v042.i08 · Cited by 4,385
Schneeweiss, S., Rassen, J. A., Glynn, R. J., Avorn, J., Mogun, H., & Brookhart, M. A. (2009) · Epidemiology · doi:10.1097/ede.0b013e3181a663cc · Cited by 1,128
Comparison of Multivariate Matching Methods: Structures, Distances, and Algorithms
Gu, X. S., & Rosenbaum, P. R. (1993) · Journal of Computational and Graphical Statistics · doi:10.1080/10618600.1993.10474623 · Cited by 702
Zhao, Z. (2004) · Review of Economics and Statistics · doi:10.1162/003465304323023705 · Cited by 389
Research works printed but not linked: 28
- Hade, 2014, Bias associated with using the estimated propensity score as a regression covariate, Stat Med, 33, 74, 10.1002/sim.5884 · DOI
- Pearl, 2010, The foundations of causal inference, Sociol Methodol, 40, 75, 10.1111/j.1467-9531.2010.01228.x · DOI
- Wu, 2015, Application of propensity-score matching in four leading medical journals, Epidemiology, 26, e19, 10.1097/EDE.0000000000000249 · DOI
- D’Agostino, 1998, Propensity score methods for bias reduction in the comparison of a treatment to a non-randomized control group, Stat Med, 17, 2265, 10.1002/(SICI)1097-0258(19981015)17:19<2265::AID-SIM918>3.0.CO;2-B · DOI
- Rosenbaum, 1983, The central role of the propensity score in observational studies for causal effects, Biometrika, 70, 41, 10.1093/biomet/70.1.41 · DOI · Paper page
- Desai, 2017, A propensity-score-based fine stratification approach for confounding adjustment when exposure is infrequent, Epidemiology, 28, 249, 10.1097/EDE.0000000000000595 · DOI
- Rassen, 2012, One-to-many propensity score matching in cohort studies, Pharmacoepidemiol Drug Saf, 21, 69, 10.1002/pds.3263 · DOI
- Pan, 2015, Propensity Score Analysis: Fundamentals and Developments
- Iacus, 2012, Causal inference without balance checking: coarsened exact matching, Polit Anal, 20, 1, 10.1093/pan/mpr013 · DOI · Paper page
- Mielke, 2007, Permutation Methods: A Distance Function Approach, 10.1007/978-0-387-69813-7 · DOI
- Greevy, 2004, Optimal multivariate matching before randomization, Biostatistics, 5, 263, 10.1093/biostatistics/5.2.263 · DOI
- Hill, 2008, Discussion of research using propensity-score matching: comments on ‘A critical appraisal of propensity-score matching in the medical literature between 1996 and 2003’ by Peter Austin, Statistics in Medicine, Stat Med, 27, 2055, 10.1002/sim.3245 · DOI
- Imai, 2008, Misunderstandings between experimentalists and observationalists about causal inference, J R Stat Soc Ser A Stat Soc, 171, 481, 10.1111/j.1467-985X.2007.00527.x · DOI
- King, 2007, When can history be our guide? The pitfalls of counterfactual inference, Int Stud Q, 51, 183, 10.1111/j.1468-2478.2007.00445.x · DOI · Paper page
- Imai, 2009, The essential role of pair matching in cluster-randomized experiments, with application to the Mexican Universal Health Insurance evaluation, Stat Sci, 24, 29, 10.1214/08-STS274 · DOI
- Petri, 1991, Channeling bias in the interpretation of drug effects, Stat Med, 10, 577, 10.1002/sim.4780100409 · DOI
- Patorno, 2014, Studies with many covariates and few outcomes: selecting covariates and implementing propensity-score-based confounding adjustments, Epidemiology, 25, 268, 10.1097/EDE.0000000000000069 · DOI
- Brookhart, 2006, Evaluating short-term drug effects using a physician-specific prescribing preference as an instrumental variable, Epidemiology, 17, 268, 10.1097/01.ede.0000193606.58671.c5 · DOI
- Schneeweiss, 2006, Simultaneous assessment of short-term gastrointestinal benefits and cardiovascular risks of selective cyclooxygenase 2 inhibitors and nonselective nonsteroidal antiinflammatory drugs: an instrumental variable analysis, Arthritis Rheum, 54, 3390, 10.1002/art.22219 · DOI
- Bateman, 2015, Statins and congenital malformations: cohort study, BMJ, 350, 10.1136/bmj.h1035 · DOI · Paper page
- Huybrechts, 2014, Antidepressant use in pregnancy and the risk of cardiac defects, N Engl J Med, 370, 2397, 10.1056/NEJMoa1312828 · DOI
- Ho, 2007, Matching as nonparametric preprocessing for reducing model dependence in parameteric causal inference, Polit Anal, 15, 199, 10.1093/pan/mpl013 · DOI
- Franklin, 2014, Metrics for covariate balance in cohort studies of causal effects, Stat Med, 33, 1685, 10.1002/sim.6058 · DOI
- Harrell, 1982, Evaluating the yield of medical tests, JAMA, 247, 2543, 10.1001/jama.1982.03320430047030 · DOI · Paper page
- Harrell, 1984, Regression modelling strategies for improved prognostic prediction, Stat Med, 3, 143, 10.1002/sim.4780030207 · DOI
- Oakes, 2017, Methods in Social Epidemiology
- Rubin, 1979, Using multivariate matched sampling and regression adjustment to control bias in observational studies, J Am Stat Assoc, 74, 318, 10.1080/01621459.1979.10482513 · DOI
- King, 2017, The balance-sample size frontier in matching methods for causal inference, Am J Polit Sci, 61, 473, 10.1111/ajps.12272 · DOI
Cited by
Patorno, E., Pawar, A., Franklin, J. M., Najafzadeh, M., Déruaz-Luyet, A., Brodovicz, K. G., Sambevski, S., Bessette, L. G., Santiago Ortiz, A. J., Kulldorff, M., et al. (2019) · Circulation · doi:10.1161/circulationaha.118.039177 · Cited by 215
Self-citationPatorno, E., Htoo, P. T., Glynn, R. J., Schneeweiss, S., Wexler, D. J., Pawar, A., Bessette, L. G., Chin, K., Everett, B. M., & Kim, S. C. (2021) · Annals of Internal Medicine · doi:10.7326/m21-0893 · Cited by 103
Tang, H., Lu, Y., Donahoo, W. T., Shao, H., Shi, L., Fonseca, V. A., Guo, Y., Bian, J., & Guo, J. (2024) · Annals of Internal Medicine · doi:10.7326/m24-0329 · Cited by 40
Deeken, C. R., Rosen, M. J., Poulose, B. K., Bradbury, K., Huang, L.-C., Ma, J., & Badhwar, A. (2023) · Frontiers in Surgery · doi:10.3389/fsurg.2023.1280991 · Cited by 7
Adenuga, A. H., & Jack, C. (2025) · Ecological Indicators · doi:10.1016/j.ecolind.2025.113714 · Cited by 4
Metadata
- Authors
- Ripollone, J. E., Huybrechts, K. F., Rothman, K. J., Ferguson, R. E., & Franklin, J. M.
- Affiliations
- Department of Epidemiology, School of Public Health, Boston University, Boston, Massachusetts
- Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts
- Venue
- American Journal of Epidemiology
- Publisher
- Oxford University Press (OUP)
- Published
- 2018-05-10
- Volume
- 187
- Issue
- 9
- Pages
- 1951-1961
- ISSN
- 0002-9262
- 1476-6256
- Cited by
- 88
- References
- 7
- Language
- en
- Licence
- academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
- Funders
- National Institute of Mental Health (K01MH099141)
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Implications of the Propensity Score Matching Paradox in Pharmacoepidemiology. https://app.cochat.ai/papers/10.1093/aje/kwy078
- The paper
- Ripollone, J. E., Huybrechts, K. F., Rothman, K. J., Ferguson, R. E., & Franklin, J. M. (2018). Implications of the Propensity Score Matching Paradox in Pharmacoepidemiology. American Journal of Epidemiology. https://doi.org/10.1093/aje/kwy078
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.
