Intelligent smoothing using hierarchical Bayesian models

Epidemiology
Patrick Graham

Abstract

Hierarchical Bayesian modeling provides a flexible approach to modeling in multiparameter problems. Examples include disease mapping and spatiotemporal analysis, and multiple exposure modeling. A key feature of hierarchical Bayesian models is that prior expectations regarding model structure are embedded in a probability model that reflects uncertainty about the form of the structure that links analytical units (such as geographic areas). This results in posterior estimates that are compromises between raw data summaries and estimates that conform exactly to the prior model structure. The posterior estimates are more precise and generally have lower mean-squared error than traditional data summaries, and yet are not strictly constrained to follow a posited prior model form.

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Citations

Aug 13, 2010·Journal of Occupational Medicine and Toxicology·Peter Morfeld, Robert J McCunney
Jul 10, 2010·International Journal of Environmental Research and Public Health·Yuejen ZhaoTony Barnes
Mar 13, 2014·International Journal of Health Geographics·Rasmus HoffmannCarme Borrell
May 7, 2010·The Science of the Total Environment·Kathrin StrebelUwe Schlink
Aug 19, 2015·Health & Place·Paula SantanaJoão Ferrão
Jun 9, 2015·International Journal for Equity in Health·Paula SantanaCarme Borrell
Nov 8, 2020·Social Psychiatry and Psychiatric Epidemiology·Eiji YoshiokaYasuaki Saijo
Nov 4, 2021·Pharmacoepidemiology and Drug Safety·Jim YoungUNKNOWN North American AIDS Cohort Collaboration on Research and Design (NA-ACCORD) of IeDEA

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