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Sensitivity analyses for sparse-data problems-using weakly informative Bayesian priors.
Hamra-GB; MacLehose-RF; Cole-SR
Epidemiology 2013 Mar; 24(2):233-239
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.
Humans; Men; Women; Age-groups; Alcohols; Cancer; Epidemiology; Sampling; Exposure-levels; Exposure-limits; Models
Ghassan Hamra, Department of Epidemiology, CB# 7435, Chapel Hill, NC 27599-7435
Grant-Number-R03-OH-009800; B20130805; M082013
Issue of Publication
University of North Carolina at Chapel Hill
Page last reviewed: April 12, 2019
Content source: National Institute for Occupational Safety and Health Education and Information Division