Commentary : Practical advantages of Bayesian anal... Catalogue en ligne

Titre :	Commentary : Practical advantages of Bayesian analysis of epidemiologic data. (2001)
Auteurs :	David-B DUNSON
Type de document :	Article
Dans :	American journal of epidemiology (vol. 153, n° 12, 2001)
Pagination :	1222-1226
Langues:	Anglais
Mots-clés :	Homme ; Méthodologie ; Variable épidémiologique ; Epidémiologie ; Distribution ; Probabilité
Résumé :	[BDSP. Notice produite par INIST-CNRS mQbQR0xL. Diffusion soumise à autorisation]. In the past decade, there have been enormous advances in the use of Bayesian methodology for analysis of epidemiologic data, and there are now many practical advantages to the Bayesian approach. Bayesian models can easily accommodate unobserved variables such as an individual's true disease status in the presence of diagnostic error. The use of prior probability distributions represents a powerful mechanism for incorporating information from previous studies and for controlling confounding. Posterior probabilities can be used as easily interpretable alternatives to p values. Recent developments in Markov chain Monte Carlo methodology facilitate the implementation of Bayesian analyses of complex data sets containing missing observations and multidimensional outcomes. Tools are now available that allow epidemiologists to take advantage of this powerful approach to assessment of exposure-disease relations. Am J Epidemiol 2001 ; 153 : 1222-6.

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