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Dose-Response and Trend Analysis in Epidemiology: Alternatives to Categorical Analysis
Abstract
Standard categorical analysis is based on an unrealistic model for dose-response and trends and does not make efficient use of within-category information. This paper describes two classes of simple alternatives that can be implemented with any regression software: fractional polynomial regression and spline regression. These methods are illustrated in a problem of estimating historical trends in human immunodeficiency virus incidence. Fractional polynomial and spline regression are especially valuable when important nonlinearities are anticipated and software for more general nonparametric regression approaches is not available.
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Cited by
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SHAHAR, E., WHITNEY, C. W., REDLINE, S., LEE, E. T., NEWMAN, A. B., JAVIER NIETO, F., O'CONNOR, G. T., BOLAND, L. L., SCHWARTZ, J. E., & SAMET, J. M. (2001) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/ajrccm.163.1.2001008 · Cited by 2,901
Meta-Analysis: High-Dosage Vitamin E Supplementation May Increase All-Cause Mortality
Miller, E. R., Pastor-Barriuso, R., Dalal, D., Riemersma, R. A., Appel, L. J., & Guallar, E. (2005) · Annals of Internal Medicine · doi:10.7326/0003-4819-142-1-200501040-00110 · Cited by 2,577
Sordo, L., Barrio, G., Bravo, M. J., Indave, B. I., Degenhardt, L., Wiessing, L., Ferri, M., & Pastor-Barriuso, R. (2017) · BMJ · doi:10.1136/bmj.j1550 · Cited by 1,816
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Metadata
- Authors
- Greenland, S.
- Venue
- Epidemiology
- Published
- 1995-07
- Volume
- 6
- Issue
- 4
- Pages
- 356-365
- ISSN
- 1044-3983 (ISSN-L)
- OpenAlex
- W2039493274
- Cited by
- 1,085
- References
- 0
- FWCI
- 17.35
- Type
- journal article
- Language
- en
- Open access
- closed
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- Greenland, S. (1995). Dose-Response and Trend Analysis in Epidemiology: Alternatives to Categorical Analysis. Epidemiology. https://doi.org/10.1097/00001648-199507000-00005
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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.
