The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Evaluating the Impact of Database Heterogeneity on Observational Study Results
Open accessAbstract
Clinical studies that use observational databases to evaluate the effects of medical products have become commonplace. Such studies begin by selecting a particular database, a decision that published papers invariably report but do not discuss. Studies of the same issue in different databases, however, can and do generate different results, sometimes with strikingly different clinical implications. In this paper, we systematically study heterogeneity among databases, holding other study methods constant, by exploring relative risk estimates for 53 drug-outcome pairs and 2 widely used study designs (cohort studies and self-controlled case series) across 10 observational databases. When holding the study design constant, our analysis shows that estimated relative risks range from a statistically significant decreased risk to a statistically significant increased risk in 11 of 53 (21%) of drug-outcome pairs that use a cohort design and 19 of 53 (36%) of drug-outcome pairs that use a self-controlled case series design. This exceeds the proportion of pairs that were consistent across databases in both direction and statistical significance, which was 9 of 53 (17%) for cohort studies and 5 of 53 (9%) for self-controlled case series. Our findings show that clinical studies that use observational databases can be sensitive to the choice of database. More attention is needed to consider how the choice of data source may be affecting results.
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Cites
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Cited by
George, H., Duke Jon, D., Shah Nigam, H., Reich Christian, G., Vojtech, H., Schuemie Martijn, J., Suchard Marc, A., Woong, P. R., Kei, W. I. C., Rijnbeek Peter, R., et al. (2015) · Studies in Health Technology and Informatics · doi:10.3233/978-1-61499-564-7-574 · Cited by 1,107
“OMOP previously demonstrated that databases from different sources can give vastly different answers [9].”
BackgroundAgniel, D., Kohane, I. S., & Weber, G. M. (2018) · BMJ · doi:10.1136/bmj.k1479 · Cited by 391
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Self-citationCharacterizing treatment pathways at scale using the OHDSI network
Hripcsak, G., Ryan, P. B., Duke, J. D., Shah, N. H., Park, R. W., Huser, V., Suchard, M. A., Schuemie, M. J., DeFalco, F. J., Perotte, A., et al. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1510502113 · Cited by 326
Self-citationSurgical research using national databases
Alluri, R. K., Leland, H., & Heckmann, N. (2016) · Annals of Translational Medicine · doi:10.21037/atm.2016.10.49 · Cited by 266
Metadata
- Authors
- Madigan, D., Ryan, P. B., Schuemie, M., Stang, P. E., Overhage, J. M., Hartzema, A. G., Suchard, M. A., DuMouchel, W., & Berlin, J. A.
- Venue
- American Journal of Epidemiology
- Publisher
- Oxford University Press (OUP)
- Published
- 2013-05-05
- Volume
- 178
- Issue
- 4
- Pages
- 645-651
- ISSN
- 0002-9262 (ISSN-L)
- 1476-6256
- OpenAlex
- W2131801584
- Cited by
- 181
- References
- 10
- FWCI
- 22.43
- Type
- journal article
- Language
- en
- Open access
- green
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- Madigan, D., Ryan, P. B., Schuemie, M., Stang, P. E., Overhage, J. M., Hartzema, A. G., Suchard, M. A., DuMouchel, W., & Berlin, J. A. (2013). Evaluating the Impact of Database Heterogeneity on Observational Study Results. American Journal of Epidemiology. https://doi.org/10.1093/aje/kwt010
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