Prevalence odds ratio or prevalence ratio in the analysis of cross sectional data : what is to be done ?
Objectives-To review the appropriateness of the prevalence odds ratio (POR) and the prevalence ratio (PR) as effect measures in the analysis of cross sectional data and to evaluate different models for the multivariate estimation of the PR.
Methods-A system of linear differential equations corresponding to a dynamic model of a cohort with a chronic disease was developed.
At any point in time, a cross sectional analysis of the people then in the cohort provided a prevalence based measure of the effect of exposure on disease.
This formed the basis for exploring the relations between the POR, the PR, and the incidence rate ratio (IRR).
Examples illustrate relations for various IRRs, prevalences, and differential exodus rates.
Multivariate point and interval estimation of the PR by logistic regression is illustrated and compared with the results from proportional hazards regression (PH) and generalised linear modelling (GLM).
The POR is difficult to interpret without making restrictive assumptions and the POR and PR may lead to different conclusions with regard to confounding and effect modification.
The PR is always conservative relative to the IRR and, if PR>1, the POR is always>PR.
In a fixed cohort and with an adverse exposure, the POR is always =IRR, but in a dynamic cohort with sufficient underlying follow up the POR may overestimate or underestimate the IRR, depending on the duration of follow up. (...)
Mots-clés Pascal : Etude transversale, Maladie, Chronique, Homme, Régression logistique, Prévalence, Incidence, Etude statistique
Mots-clés Pascal anglais : Cross sectional study, Disease, Chronic, Human, Logistic regression, Prevalence, Incidence, Statistical study
Notice produite par :
Inist-CNRS - Institut de l'Information Scientifique et Technique
Cote : 98-0232455
Code Inist : 002B30A01A1. Création : 11/09/1998.