Cited by 1,128Advanced Causal Inference TechniquesStatistical Methods in EpidemiologyMedical Coding and Health Information

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High-dimensional propensity score adjustment in studies of treatment effects using health care claims data.

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Schneeweiss, Sebastian, Rassen, Jeremy A., Glynn, Robert J., Avorn, Jerry, Mogun, Helen, Brookhart, M Alan·2009·Epidemiology

DOI 10.1097/ede.0b013e3181a663ccPMC PMC3077219

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Abstract

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.

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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
ORCID: Schneeweiss, Sebastian
Rassen, Jeremy A.
Glynn, Robert J.
Avorn, Jerry
Brookhart, M Alan
Venue: Epidemiology
Publisher: Ovid Technologies (Wolters Kluwer Health)
Published: 2009-07
Volume: 20
Issue: 4
Pages: 512-522
ISSN: 1044-3983 (ISSN-L)
DOI: 10.1097/ede.0b013e3181a663cc
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

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High-dimensional propensity score adjustment in studies of treatment effects using health care claims data.

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