Historique
Périodique
Capture-recapture models including covariate effects
Auteur(s) :
K. TILLING ;
STERNE J. A.
Article en page(s) :
392-400
Domaine :
Hors addiction / No addiction
Langue(s) :
Anglais
Discipline :
EPI (Epidémiologie / Epidemiology)
Thésaurus mots-clés
MODELE STATISTIQUE
;
CAPTURE-RECAPTURE
;
EPIDEMIOLOGIE ANALYTIQUE
;
METHODE
;
COHORTE
Note générale :
American Journal of Epidemiology, 1999, 149, (4), 392-400
Résumé :
ENGLISH :
Capture-recapture methods are used to estimate the incidence of a disease, using a multiple-source registry. Usually, log-linear methods are used to estimate population size, assuming that not all sources of notification are dependent. Where there are categorical covariates, a stratified analysis can be performed. The multinomial logit model has occasionally been used. In this paper, the authors compare log-linear and logit models with and without covariates, and use simulated data to compare estimates from different models. The crude estimate of population size is biased when the sources are not independent. Analyses adjusting for covariates produce less biased estimates. In the absence of covariates, or where all covariates are categorical, the log-linear model and the logit model are equivalent. The log-linear model cannot include continuous variables. To minimize potential bias in estimating incidence, covariates should be included in the design and analysis of multiple-source disease registries. (Author' s abstract)
ENGLISH :
Capture-recapture methods are used to estimate the incidence of a disease, using a multiple-source registry. Usually, log-linear methods are used to estimate population size, assuming that not all sources of notification are dependent. Where there are categorical covariates, a stratified analysis can be performed. The multinomial logit model has occasionally been used. In this paper, the authors compare log-linear and logit models with and without covariates, and use simulated data to compare estimates from different models. The crude estimate of population size is biased when the sources are not independent. Analyses adjusting for covariates produce less biased estimates. In the absence of covariates, or where all covariates are categorical, the log-linear model and the logit model are equivalent. The log-linear model cannot include continuous variables. To minimize potential bias in estimating incidence, covariates should be included in the design and analysis of multiple-source disease registries. (Author' s abstract)
Affiliation :
Department of Public Health Sciences, Guy's, King's and St Thomas's School of Medicine, London
Royaume-Uni. United Kingdom.
Royaume-Uni. United Kingdom.
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