Hierarchical latency models for dose-time-response associations.
Richardson-DB; MacLehose-RF; Langholz-B; Cole-SR
Am J Epidemiol 2011 Mar; 173(6):695-702
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.
Exposure-levels; Diseases; Analytical-processes; Risk-analysis; Risk-factors; Lung; Lung-cancer; Asbestos-products; Textile-workers; Uranium-compounds; Uranium-miners; Dose-response; Models; Mathematical-models; Computer-models; Statistical-analysis;
Author Keywords: cohort studies; hierarchical model; latency; neoplasms; regression
David B. Richardson, Department of Epidemiology, School of Public Health, University of North Carolina, Chapel Hill, NC 27599-7435
American Journal of Epidemiology
University of Nevada, Reno