© 1998 Oxford University Press
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Prevalence proportion ratios: estimation and hypothesis testing
aNational Institute of Occupational Health Lersø Parkalle 105. DK.2100 København ø, Denmark
bNational Institute for Occupational Safety and Health, Division of Surveillance, Hazard Evaluations and Field Studies 4676 Columbia Parkway, Cincinnati, OH 45226-1988, USA
cDepartment of Mathematical Sciences, University of Cincinnati Cincinnati, OH 45221, USA
BACKGROUND: 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.
METHODS: Five sets of simulations representing fourteen separate simulation conditions were used to test the performance of the methods.
RESULTS: All three models produced point estimates close to the true parameter, i.e. the estimators of the parameter associated with exposure had negligible bias. The Cox regression produced standard errors that were too large, especially when the prevalence of the disease was high, whereas the log-binomial model and the GEE- logistic model had the correct type I error probabilities. It was shown by example that the GEE-logistic model could produce prevalences greater than one, whereas it was proven that this could not happen with the log-binomial model. The log- binomial model should be preferred.
Keywords Generalized linear model, Cox regression, cross sectional study, log-binomial model, GEE-logistic model
Accepted 28 May 1997
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