Display Settings:

Format

Send to:

Choose Destination
See comment in PubMed Commons below
Int J Epidemiol. 1998 Feb;27(1):91-5.

Prevalence proportion ratios: estimation and hypothesis testing.

Author information

  • 1National Institute of Occupational Health, KĂžbenhavn O, Denmark.

Abstract

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.

Comment in

PMID:
9563700
[PubMed - indexed for MEDLINE]
Free full text
PubMed Commons home

PubMed Commons

0 comments
How to join PubMed Commons

    Supplemental Content

    Icon for HighWire
    Loading ...
    Write to the Help Desk