The DOI matches its Crossref record; 61 of the 61 references it cites were found in a registry or an index.
Confounding Control in Healthcare Database Research: Challenges and Potential Approaches
Open accessAbstract
Epidemiologic studies are increasingly used to investigate the safety and effectiveness of medical products and interventions. Appropriate adjustment for confounding in such studies is challenging because exposure is determined by a complex interaction of patient, physician, and healthcare system factors. The challenges of confounding control are particularly acute in studies using healthcare utilization databases where information on many potential confounding factors is lacking and the meaning of variables is often unclear. We discuss advantages and disadvantages of different approaches to confounder control in healthcare databases. In settings where considerable uncertainty surrounds the data or the causal mechanisms underlying the treatment assignment and outcome process, we suggest that researchers report a panel of results under various specifications of statistical models. Such reporting allows the reader to assess the sensitivity of the results to model assumptions that are often not supported by strong subject-matter knowledge.
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
Charlson, M. E., Pompei, P., Ales, K. L., & MacKenzie, C. R. (1987) · Journal of Chronic Diseases · doi:10.1016/0021-9681(87)90171-8 · Cited by 49,742
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
Marginal Structural Models and Causal Inference in Epidemiology
Robins, J. M., Hernán, M. Á., & Brumback, B. (2000) · Epidemiology · doi:10.1097/00001648-200009000-00011 · Cited by 5,581
Identification of Causal Effects Using Instrumental Variables
Angrist, J. D., Imbens, G. W., & Rubin, D. B. (1996) · Journal of the American Statistical Association · doi:10.1080/01621459.1996.10476902 · Cited by 4,230
Causal Diagrams for Epidemiologic Research
Greenland, S., Pearl, J., & Robins, J. M. (1999) · Epidemiology · doi:10.1097/00001648-199901000-00008 · Cited by 3,728
Air Pollution and Hospital Admissions for Heart Disease in Eight U.S. Counties
Schwartz, J. (1999) · Epidemiology · doi:10.1097/00001648-199901000-00005 · Cited by 3,038
Estimating Causal Effects from Large Data Sets Using Propensity Scores
Rubin, D. B. (1997) · Annals of Internal Medicine · doi:10.7326/0003-4819-127-8_part_2-199710151-00064 · Cited by 2,877
Causal diagrams for empirical research
PEARL, J. (1995) · Biometrika · doi:10.1093/biomet/82.4.669 · Cited by 2,355 · cited 1×
“The theory of causal directed acyclic graphs provides a theoretical framework for using subject-matter knowledge to identify minimal sets of variables that must be included in a statistical model to eliminate confounding bias.(2,3) These graphs require that the analyst specify the causal relations between every releva…”
BackgroundVariable 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-citationOveradjustment bias and unnecessary adjustment in epidemiologic studies.
Schisterman, E. F., Cole, S. R., & Platt, R. W. (2009) · Epidemiology · doi:10.1097/ede.0b013e3181a819a1 · Cited by 2,096
Cited by
Interpretation of the evidence for the efficacy and safety of statin therapy
Collins, R., Reith, C., Emberson, J., Armitage, J., Baigent, C., Blackwell, L., Blumenthal, R., Danesh, J., Smith, G. D., DeMets, D., et al. (2016) · The Lancet · doi:10.1016/s0140-6736(16)31357-5 · Cited by 1,857
Lund, J. L., Richardson, D. B., & Stürmer, T. (2015) · Current Epidemiology Reports · doi:10.1007/s40471-015-0053-5 · Cited by 739
Self-citationVoss, E. A., Makadia, R., Matcho, A., Ma, Q., Knoll, C., Schuemie, M., DeFalco, F. J., Londhe, A., Zhu, V., & Ryan, P. B. (2015) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocu023 · Cited by 339
Antidepressant dose, age, and the risk of deliberate self-harm
Miller, M., Swanson, S. A., Azrael, D., Pate, V., & Stürmer, T. (2014) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2014.1053 · Cited by 92
Self-citationApproaches to Comparative Effectiveness Research in Multimorbid Populations
Maciejewski, M. L., & Bayliss, E. A. (2014) · Medical Care · doi:10.1097/mlr.0000000000000060 · Cited by 24
Metadata
- Authors
- Brookhart, M. A., Stürmer, T., Glynn, R. J., Rassen, J., & Schneeweiss, S.
- Venue
- Medical Care
- Published
- 2010-06
- Volume
- 48
- Issue
- 6
- Pages
- S114-S120
- ISSN
- 0025-7079 (ISSN-L)
- OpenAlex
- W1984454461
- Cited by
- 377
- References
- 61
- FWCI
- 20.47
- Type
- journal article
- Language
- en
- Open access
- green
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: Confounding Control in Healthcare Database Research: Challenges and Potential Approaches. https://app.cochat.ai/papers/10.1097/mlr.0b013e3181dbebe3
- The paper
- Brookhart, M. A., Stürmer, T., Glynn, R. J., Rassen, J., & Schneeweiss, S. (2010). Confounding Control in Healthcare Database Research: Challenges and Potential Approaches. Medical Care. https://doi.org/10.1097/mlr.0b013e3181dbebe3
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.
