The DOI matches its Crossref record; 19 of the 20 references it cites were found in a registry or an index.
Making inferences on treatment effects from real world data: propensity scores, confounding by indication, and other perils for the unwary in observational research
Open accessAbstract
Propensity score based methods are used increasingly to evaluate the effectiveness of treatments when evidence from randomised trials is not available. However, users need to be aware of their strengths and limitations
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
“Hence, the prognosis of those receiving the (drug) treatment may be better or worse than those who are not, but information on prognosis and/or severity of the underlying illness may not be available in the data [72].”
From The Reporting of Studies Conducted Using Observational Routinely-Collected Health Data (RECORD) statement. (2015) · cites this paper as [72]
In the citing paper
“A particular type of unmeasured confounding is confounding by indication; this is often an issue when examining effectiveness and safety of (drug) treatments using routinely collected data.”
From The Reporting of Studies Conducted Using Observational Routinely-Collected Health Data (RECORD) statement. (2015) · cites this paper as [72]
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 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
The Effect of Spironolactone on Morbidity and Mortality in Patients with Severe Heart Failure
Pitt, B., Zannad, F., Remme, W. J., Cody, R., Castaigne, A., Perez, A., Palensky, J., & Wittes, J. (1999) · New England Journal of Medicine · doi:10.1056/nejm199909023411001 · Cited by 9,214
Fisher, R. A. (1936) · BMJ · doi:10.1136/bmj.1.3923.554-a · Cited by 4,980
Pitt, B., Remme, W., Zannad, F., Neaton, J., Martinez, F., Roniker, B., Bittman, R., Hurley, S., Kleiman, J., & Gatlin, M. (2003) · New England Journal of Medicine · doi:10.1056/nejmoa030207 · Cited by 4,941
A comparison of rate control and rhythm control in patients with atrial fibrillation
(2002) · New England Journal of Medicine · doi:10.1056/nejmoa021328 · Cited by 4,405
The Central Role of the Propensity Score in Observational Studies for Causal Effects
Rosenbaum, P. R., & Rubin, D. B. (1983) · Biometrika · doi:10.2307/2335942 · Cited by 3,495
Eplerenone in patients with systolic heart failure and mild symptoms
Zannad, F., McMurray, J. J. V., Krum, H., van Veldhuisen, D. J., Swedberg, K., Shi, H., Vincent, J., Pocock, S. J., & Pitt, B. (2011) · New England Journal of Medicine · doi:10.1056/nejmoa1009492 · Cited by 3,111
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
The Risk Associated with Aprotinin in Cardiac Surgery
Mangano, D. T., Tudor, I. C., & Dietzel, C. (2006) · New England Journal of Medicine · doi:10.1056/nejmoa051379 · Cited by 1,101
Mortality Associated With Aprotinin During 5 Years Following Coronary Artery Bypass Graft Surgery
Mangano, D. T. (2007) · JAMA · doi:10.1001/jama.297.5.471 · Cited by 394
Research works printed but not linked: 2
- For personal use only: See rights and reprints http://www.bmj.com/permissions Subscribe: http://www.bmj.com/subscribe RESEARCH METHODS & REPORTING on 5 November 2023 by guest. Protected by copyright. http://www.bmj.com/ BMJ: first published as 10.1136/bmj.f6409 on 11 November 2013. Downloaded from 9 Trends in the characteristics, treatment and prognosis of people with symptomatic heart failure and the use of aldosterone antagonists: analysis plan. www.ucl.ac.uk/priment/ documents/Heart-Failure-Analysis-Plan.
- Saksena S, Slee A, Waldo AL, Freemantle N, Reynolds M, Rosenberg Y, et al. Cardiovascular outcomes in the AFFIRM trial: an assessment of individual antiarrhythmic drug therapies compared to rate control using propensity score matched analyses. J Am Clin Cardiol 2011;19:1975-85.
Cited by
Sorted by citations
Benchimol, E. I., Smeeth, L., Guttmann, A., Harron, K., Moher, D., Petersen, I., Sørensen, H. T., von Elm, E., Langan, S. M., & RECORD Working Committee (2015) · PLOS Medicine · doi:10.1371/journal.pmed.1001885 · Cited by 5,749
Self-citationInterpretation and Impact of Real-World Clinical Data for the Practicing Clinician
Blonde, L., Khunti, K., Harris, S. B., Meizinger, C., & SKOLNIK, N. (2018) · Advances in Therapy · doi:10.1007/s12325-018-0805-y · Cited by 757
Liddle, A. D., Judge, A., Pandit, H. G., & Murray, D. W. (2014) · The Lancet · doi:10.1016/s0140-6736(14)60419-0 · Cited by 676
Mansournia, M. A., & Altman, D. G. (2016) · BMJ · doi:10.1136/bmj.i189 · Cited by 434
Smith, A., Crouch, S., Lax, S. J., Li, J., Painter, D., Howell, D. A., Patmore, R. D., Jack, A., & Roman, E. (2015) · British Journal of Cancer · doi:10.1038/bjc.2015.94 · Cited by 427
Metadata
- Authors
- Freemantle, N., Marston, L., Walters, K., Wood, J., Reynolds, M. R., & Petersen, I.
- Affiliations
- The Royal Free Hospital (GB)
- Lahey Hospital and Medical Center (US)
- Venue
- BMJ
- Publisher
- BMJ
- Published
- 2013-11-11
- Volume
- 347
- Issue
- nov11 3
- Pages
- f6409-f6409
- ISSN
- 1756-1833 (ISSN-L)
- OpenAlex
- W1989803330
- Cited by
- 211
- References
- 20
- Citations by year
- 2026: 5 · 2025: 10 · 2024: 7 · 2023: 18 · 2022: 23 · 2021: 14 · 2020: 26 · 2019: 15 · 2018: 28 · 2017: 22 · 2016: 23 · 2015: 15 · 2014: 5
- FWCI
- 12.61
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- BMJ (publishedVersion)
- PubMed (publishedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Keywords
- propensity score, confounding by indication, real-world data, observational studies, treatment effect estimation, causal inference, effectiveness evaluation
- MeSH terms
- Humans, Research, Research Design, Reproducibility of Results, Bias, Confounding Factors, Epidemiologic, Randomized Controlled Trials as Topic, Treatment Outcome, Propensity Score, Observational Studies as Topic
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
