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Prevalence proportion ratios: estimation and hypothesis testing.
Skov T; Deddens J; Petersen MR; Endahl L
Int J Epidemiol 1998 Feb; 27(1):91-95
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. Five sets of simulations representing fourteen separate simulation conditions were used to test the performance of the methods. 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.
Models; Group-dynamics; Musculoskeletal-system-disorders; Simulation-methods; Exposure-assessment; Statistical-analysis; Author Keywords: Generalized linear model; Cox regression; cross sectional study; log-binomial model; GEE-logistic model
National Institute for Occupational Safety and Health, Division of Surveillance, Hazard Evaluations and Field Studies, 4676 Columbia Parkway, Cincinnati, Ohio 45226-1988, USA
Issue of Publication
International Journal of Epidemiology
Page last reviewed: September 2, 2020
Content source: National Institute for Occupational Safety and Health Education and Information Division