| Titre : | Hierarchical Latency Models for Dose-Time-Response Associations. (2011) |
| Auteurs : | David-B RICHARDSON ; Stephen-R COLE ; LANGHOLZ (Bryan) : USA. Division of Biostatistics. Department of Preventive Medicine. Keck School of Medicine. University of Southern California. Los Angeles. CA. ; MACLEHOSE (Richard-F) : USA. Division of Epidemiology and Community Health and Division of Biostatistics. School of Public Health. University of Minnesota. Minneapolis. MN. |
| Type de document : | Article |
| Dans : | American journal of epidemiology (vol. 173, n° 6, 2011) |
| Pagination : | 695-702 |
| Langues: | Anglais |
| Mots-clés : | Tumeur ; Modèle ; Dosage ; Association ; Régression ; Epidémiologie |
| Résumé : | [BDSP. Notice produite par INIST-CNRS AR0xAp9B. Diffusion soumise à autorisation]. Exposure lagging and exposure-time window analysis are 2 widely used approaches to allow for induction and latency periods in analyses of exposure-disease associations. Exposure lagging implies a strong parametric assumption about the temporal evolution of the exposure-disease association. An exposure-time window analysis allows for a more flexible description of temporal variation in exposure effects but may result in unstable risk estimates that are sensitive to how windows are defined. The authors describe a hierarchical regression approach that combines time window analysis with a parametric latency model. They illustrate this approach using data from 2 occupational cohort studies : studies of lung cancer mortality among 1) asbestos textile workers and 2) uranium miners. For each cohort, an exposure-time window analysis was compared with a hierarchical regression analysis with shrinkage toward a simpler, second-stage parametric latency model. In each cohort analysis, there is substantial stability gained in time window-specific estimates of association by using a hierarchical regression approach. The proposed hierarchical regression model couples a time window analysis with a parametric latency model ; this approach provides a way to stabilize risk estimates derived from a time window analysis and a way to reduce bias arising from misspecification of a parametric latency model. |
Service Documentation EHESP
Me connecter
PORTAIL DOCUMENTAIRE
École des hautes études en santé publique
Catalogue
Multi-bases
Accueil
Sélection de la langue
Adresse
Service Documentation EHESPBibliothèque Rennes Avenue du Professeur Léon Bernard – CS 74312 – 35043 Rennes Cedex Bibliothèque MSSH Paris 20 avenue George Sand - 93210 La Plaine Saint-Denis
France
Bibliothèque Rennes : 02 99 02 29 50 Bibliothèque MSSH Paris : 02 99 02 24 50
contact