Prevalence proportion ratios: estimation and hypothesis testing | Zendy

T. skove | Zendy; James A. Deddens | Zendy; Martin R. Petersen | Zendy; Lars Endahl | Zendy

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Prevalence proportion ratios: estimation and hypothesis testing

Author(s) -

T. skove,

James A. Deddens,

Martin R. Petersen,

Lars Endahl

Publication year - 1998

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/27.1.91

Subject(s) - logistic regression , statistics , binomial regression , gee , generalized estimating equation , generalized linear model , regression analysis , estimation , econometrics , mathematics , standard error , multiplicative function , engineering , mathematical analysis , systems engineering

Recent communications have argued that often it may not be appropriate to analyse cross-sectional studies of prevalent outcomes with logistic regression models. The purpose of this communication is to compare three methods that have been proposed for application to cross sectional studies: (1) a multiplicative generalized linear model, which we will call the log-binomial model, (2) a method based on logistic regression and robust estimation of standard errors, which we will call the GEE-logistic model, and (3) a Cox regression model.

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