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BINOMIAL REGRESSION IN GLIM: ESTIMATING RISK RATIOS AND RISK DIFFERENCES1

WACHOLDER, SHOLOM·1986·American Journal of Epidemiology

Abstract

Although an estimate of the odds ratio adjusted for other covariates can be obtained by logistic regression, until now there has been no simple way to estimate other interesting parameters such as the risk ratio and risk difference multivariately for prospective binomial data. These parameters can be estimated in the generalized linear model framework by choosing different link functions or transformations of binomial or binary data. Macros for use with the program GLIM provide a simple method to compute parameters other than the odds ratio while adjusting for confounding factors. A data set presented previously is used as an example.

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Metadata

Authors
WACHOLDER, S.
Venue
American Journal of Epidemiology
Published
1986-01
Volume
123
Issue
1
Pages
174-184
ISSN
  • 0002-9262 (ISSN-L)
  • 1476-6256
OpenAlex
W2144422782
Cited by
623
References
0
FWCI
12.65
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: BINOMIAL REGRESSION IN GLIM: ESTIMATING RISK RATIOS AND RISK DIFFERENCES1. https://app.cochat.ai/papers/10.1093/oxfordjournals.aje.a114212
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WACHOLDER, S. (1986). BINOMIAL REGRESSION IN GLIM: ESTIMATING RISK RATIOS AND RISK DIFFERENCES1. American Journal of Epidemiology. https://doi.org/10.1093/oxfordjournals.aje.a114212

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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.