Standardized binomial models for risk or prevalence ratios and differences | Zendy

David B. Richardson | Zendy; Alan C. Kinlaw | Zendy; Richard F. MacLehose | Zendy; Stephen R. Cole | Zendy

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Standardized binomial models for risk or prevalence ratios and differences

Author(s) -

David B. Richardson,

Alan C. Kinlaw,

Richard F. MacLehose,

Stephen R. Cole

Publication year - 2015

Publication title -

international journal of epidemiology

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.406

H-Index - 208

eISSN - 1464-3685

pISSN - 0300-5771

DOI - 10.1093/ije/dyv137

Subject(s) - statistics , medicine , odds ratio , logistic regression , binomial regression , regression analysis , cohort , confidence interval , cohort study , epidemiology , population , relative risk , regression , generalized linear model , demography , mathematics , environmental health , sociology

Epidemiologists often analyse binary outcomes in cohort and cross-sectional studies using multivariable logistic regression models, yielding estimates of adjusted odds ratios. It is widely known that the odds ratio closely approximates the risk or prevalence ratio when the outcome is rare, and it does not do so when the outcome is common. Consequently, investigators may decide to directly estimate the risk or prevalence ratio using a log binomial regression model.

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