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Integrating informative priors from experimental research with Bayesian methods: an example from radiation epidemiology.

Authors
Hamra-G; Richardson-D; Maclehose-R; Wing-S
Source
Epidemiology 2013 Jan; 24(1):90-95
NIOSHTIC No.
20042136
Abstract
Informative priors can be a useful tool for epidemiologists to handle problems of sparse data in regression modeling. It is sometimes the case that an investigator is studying a population exposed to two agents, X and Y, where Y is the agent of primary interest. Previous research may suggest that the exposures have different effects on the health outcome of interest, one being more harmful than the other. Such information may be derived from epidemiologic analyses; however, in the case where such evidence is unavailable, knowledge can be drawn from toxicologic studies or other experimental research. Unfortunately, using toxicologic findings to develop informative priors in epidemiologic analyses requires strong assumptions, with no established method for its utilization. We present a method to help bridge the gap between animal and cellular studies and epidemiologic research by specification of an order-constrained prior. We illustrate this approach using an example from radiation epidemiology.
Keywords
Epidemiology; Radiation; Mathematical-models; Analytical-models; Analytical-instruments; Data-processing; Toxic-dose; Toxic-effects; Animal-studies; Cellular-function
Contact
Ghassan Hamra, Department of Epidemiology, CB# 7435, Chapel Hill, NC 27599-7435
CODEN
EPIDEY
Publication Date
20130101
Document Type
Journal Article
Email Address
ghassan.hamra@unc.edu
Funding Type
Grant
Fiscal Year
2013
NTIS Accession No.
NTIS Price
Identifying No.
Grant-Number-R03-OH-009800; B20130221
Issue of Publication
1
ISSN
1044-3983
Source Name
Epidemiology
State
NC
Performing Organization
University of North Carolina at Chapel Hill
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