Titre :
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Hierarchical Latency Models for Dose-Time-Response Associations. (2011)
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Auteurs :
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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.
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Type de document :
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Article
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Dans :
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American journal of epidemiology (vol. 173, n° 6, 2011)
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Pagination :
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695-702
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Langues:
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Anglais
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Mots-clés :
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Tumeur
;
Modèle
;
Dosage
;
Association
;
Régression
;
Epidémiologie
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Résumé :
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[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.
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