Abstract

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

Logistic regressionStatisticsBinomial regressionGeeGeneralized estimating equationGeneralized linear modelRegression analysisEstimationEconometricsMathematicsStandard errorMultiplicative functionEngineering

MeSH Terms

Cross-Sectional StudiesDenmarkHumansLinear ModelsLogistic ModelsOdds RatioPrevalenceProportional Hazards ModelsRegression AnalysisSensitivity and Specificity

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Publication Info

Year
1998
Type
article
Volume
27
Issue
1
Pages
91-95
Citations
492
Access
Closed

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Cite This

T. skove, James A. Deddens, Martin R. Petersen et al. (1998). Prevalence proportion ratios: estimation and hypothesis testing. International Journal of Epidemiology , 27 (1) , 91-95. https://doi.org/10.1093/ije/27.1.91

Identifiers

DOI
10.1093/ije/27.1.91
PMID
9563700

Data Quality

Data completeness: 86%