The DOI matches its Crossref record; 43 of the 44 references it cites were found in a registry or an index.
High-dimensional propensity score adjustment in studies of treatment effects using health care claims data.
Open accessAbstract
BACKGROUND: Adjusting for large numbers of covariates ascertained from patients' health care claims data may improve control of confounding, as these variables may collectively be proxies for unobserved factors. Here, we develop and test an algorithm that empirically identifies candidate covariates, prioritizes covariates, and integrates them into a propensity-score-based confounder adjustment model. METHODS: We developed a multistep algorithm to implement high-dimensional proxy adjustment in claims data. Steps include (1) identifying data dimensions, eg, diagnoses, procedures, and medications; (2) empirically identifying candidate covariates; (3) assessing recurrence of codes; (4) prioritizing covariates; (5) selecting covariates for adjustment; (6) estimating the exposure propensity score; and (7) estimating an outcome model. This algorithm was tested in Medicare claims data, including a study on the effect of Cox-2 inhibitors on reduced gastric toxicity compared with nonselective nonsteroidal anti-inflammatory drugs (NSAIDs). RESULTS: In a population of 49,653 new users of Cox-2 inhibitors or nonselective NSAIDs, a crude relative risk (RR) for upper GI toxicity (RR = 1.09 [95% confidence interval = 0.91-1.30]) was initially observed. Adjusting for 15 predefined covariates resulted in a possible gastroprotective effect (0.94 [0.78-1.12]). A gastroprotective effect became stronger when adjusting for an additional 500 algorithm-derived covariates (0.88 [0.73-1.06]). Results of a study on the effect of statin on reduced mortality were similar. Using the algorithm adjustment confirmed a null finding between influenza vaccination and hip fracture (1.02 [0.85-1.21]). CONCLUSIONS: In typical pharmacoepidemiologic studies, the proposed high-dimensional propensity score resulted in improved effect estimates compared with adjustment limited to predefined covariates, when benchmarked against results expected from randomized trials.
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?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“This algorithm leverages that large volumes of covariates can collectively serve as good proxies for unmeasured confounders and thus produce results from observational data that more closely resemble results from randomization.42”
From Hysterectomy versus uterine preservation for pelvic organ prolapse surgery: a prospective cohort study (2025) · cites this paper as [42]
Experimental — accuracy not yet measured
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
(2001) · RePEc: Research Papers in Economics · Cited by 27,577
Revisiting the Behavioral Model and Access to Medical Care: Does it Matter?
Andersen, R. M. (1995) · Journal of Health and Social Behavior · doi:10.2307/2137284 · Cited by 9,831
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
Self-citationEvaluating medication effects outside of clinical trials: new-user designs
Ray, W. A. (2003) · American Journal of Epidemiology · doi:10.1093/aje/kwg231 · Cited by 1,646
A review of uses of health care utilization databases for epidemiologic research on therapeutics
Schneeweiß, S., & Avorn, J. (2005) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2004.10.012 · Cited by 1,290
Self-citationHirano, K., & Imbens, G. W. (2001) · Health Services and Outcomes Research Methodology · doi:10.1023/a:1020371312283 · Cited by 1,039
Unified Methods for Censored Longitudinal Data and Causality
van der Laan, M. J., & Robins, J. M. (2003) · Springer series in statistics · doi:10.1007/978-0-387-21700-0 · Cited by 979
Quantifying Biases in Causal Models: Classical Confounding vs Collider-Stratification Bias
Greenland, S. (2003) · Epidemiology · doi:10.1097/01.ede.0000042804.12056.6c · Cited by 810
Stürmer, T., Joshi, M., Glynn, R. J., Avorn, J., Rothman, K. J., & Schneeweiß, S. (2005) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2005.07.004 · Cited by 668
Self-citationSolomon, D. H., Schneeweiß, S., Glynn, R. J., Kiyota, Y., Levin, R., Mogun, H., & Avorn, J. (2004) · Circulation · doi:10.1161/01.cir.0000127578.21885.3e · Cited by 506
Self-citation
Flagged first, then by citations
1 of the 44 references couldn’t be matched to a known paper.
Research works printed but not linked: 1
- Arana, 2004, What do we show and who does so? An analysis of the abstracts presented at the 19th ICPE., Pharmacoepidemiol Drug Saf, 13, S330
Cited by
Sorted by citations
Using Big Data to Emulate a Target Trial When a Randomized Trial Is Not Available: Table 1.
Hernán, M. A., & Robins, J. M. (2016) · American Journal of Epidemiology · doi:10.1093/aje/kwv254 · Cited by 3,115
Long-term cardiovascular outcomes of COVID-19
Xie, Y., Xu, E., Bowe, B. C., & Al‐Aly, Z. (2022) · Nature Medicine · doi:10.1038/s41591-022-01689-3 · Cited by 2,125
Principles of confounder selection
VanderWeele, T. J. (2019) · European Journal of Epidemiology · doi:10.1007/s10654-019-00494-6 · Cited by 1,535
Taiwan’s National Health Insurance Research Database: past and future
Hsieh, C., Su, C., Shao, S., Sung, S., Lin, S., Yang, Y. K., & Lai, E. C. (2019) · Clinical Epidemiology · doi:10.2147/clep.s196293 · Cited by 1,227
A combined comorbidity score predicted mortality in elderly patients better than existing scores
Gagne, J. J., Glynn, R. J., Avorn, J., Levin, R., & Schneeweiss, S. (2011) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2010.10.004 · Cited by 995
Self-citation
Metadata
- Authors
- Schneeweiss, S., Rassen, J. A., Glynn, R. J., Avorn, J., Mogun, H., & Brookhart, M. A.
- Affiliations
- Brigham and Women's Hospital (US)
- Corresponding author
- Schneeweiss, Sebastian
- Venue
- Epidemiology
- Published
- 2009-07
- Volume
- 20
- Issue
- 4
- Pages
- 512-522
- ISSN
- 1044-3983 (ISSN-L)
- PMC
- PMC3077219
- OpenAlex
- W1967862917
- Cited by
- 1,128
- References
- 44
- Citations by year
- 2026: 51 · 2025: 78 · 2024: 77 · 2023: 62 · 2022: 71 · 2021: 104 · 2020: 87 · 2019: 86 · 2018: 69 · 2017: 73 · 2016: 72 · 2015: 62 · 2014: 61 · 2013: 66 · 2012: 55
- FWCI
- 29.60
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Epidemiology (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health (1r01lm010213-01)
- National Institute on Aging (R01AG021950, R01-AG027400, K25AG027400, R01AG018833, R01AG023178)
- National Institute of Mental Health (R01-MH078708, U01MH078708)
- U.S. National Library of Medicine (R01-LM010213)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Health Sciences › Health Professions › Health Information Management › Medical Coding and Health Information
- Keywords
- high-dimensional propensity score, confounding adjustment, health insurance claims, COX-2 inhibitors, non-selective NSAIDs, influenza vaccination, hip fracture, Medicare claims data, pharmacoepidemiology
- MeSH terms
- Aged, Aged, 80 and over, Algorithms, Anti-Inflammatory Agents, Non-Steroidal, Female, Medicare, Humans, Insurance Claim Review, Male, United States, Confounding Factors, Epidemiologic, Treatment Outcome, Pharmacoepidemiology, Risk Assessment, Upper Gastrointestinal Tract, Cyclooxygenase 2 Inhibitors
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
