Commentary: practical advantages of Bayesian analysis of epidemiologic data.
In the past decade, there have been enormous advances in the use of Bayesian methodology for analysis of epidemiologic data, and there are now many practical advantages to the Bayesian approach. Bayesian models can easily accommodate unobserved variables such as an individual's true disease status in the presence of diagnostic error. The use of prior probability distributions represents a powerful mechanism for incorporating information from previous studies and for controlling confounding. Posterior probabilities can be used as easily interpretable alternatives to p values. Recent developments in Markov chain Monte Carlo methodology facilitate the implementation of Bayesian analyses of complex data sets containing missing observations and multidimensional outcomes. Tools are now available that allow epidemiologists to take advantage of this powerful approach to assessment of exposure-disease relations.
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- Humans
- Epidemiology
- Bayes Theorem
- 4202 Epidemiology
- 11 Medical and Health Sciences
- 01 Mathematical Sciences
Citation
Published In
DOI
EISSN
ISSN
Publication Date
Volume
Issue
Start / End Page
Related Subject Headings
- Humans
- Epidemiology
- Bayes Theorem
- 4202 Epidemiology
- 11 Medical and Health Sciences
- 01 Mathematical Sciences