Skip directly to search Skip directly to A to Z list Skip directly to page options Skip directly to site content

NIOSHTIC-2 Publications Search

Search Results

Sensitivity analyses for sparse-data problems-using weakly informative Bayesian priors.

Authors
Hamra-GB; MacLehose-RF; Cole-SR
Source
Epidemiology 2013 Mar; 24(2):233-239
NIOSHTIC No.
20042991
Abstract
Sparse-data problems are common, and approaches are needed to evaluate the sensitivity of parameter estimates based on sparse data. We propose a Bayesian approach that uses weakly informative priors to quantify sensitivity of parameters to sparse data. The weakly informative prior is based on accumulated evidence regarding the expected magnitude of relationships using relative measures of disease association. We illustrate the use of weakly informative priors with an example of the association of lifetime alcohol consumption and head and neck cancer. When data are sparse and the observed information is weak, a weakly informative prior will shrink parameter estimates toward the prior mean. Additionally, the example shows that when data are not sparse and the observed information is not weak, a weakly informative prior is not influential. Advancements in implementation of Markov Chain Monte Carlo simulation make this sensitivity analysis easily accessible to the practicing epidemiologist.
Keywords
Humans; Men; Women; Age-groups; Alcohols; Cancer; Epidemiology; Sampling; Exposure-levels; Exposure-limits; Models
Contact
Ghassan Hamra, Department of Epidemiology, CB# 7435, Chapel Hill, NC 27599-7435
CODEN
EPIDEY
Publication Date
20130301
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; B20130805; M082013
Issue of Publication
2
ISSN
1044-3983
Source Name
Epidemiology
State
NC; MN
Performing Organization
University of North Carolina at Chapel Hill
TOP