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Markov chain Monte Carlo: an introduction for epidemiologists.
Hamra-G; MacLehose-R; Richardson-D
Int J Epidemiol 2013 Apr; 42(2):627-634
Markov Chain Monte Carlo (MCMC) methods are increasingly popular among epidemiologists. The reason for this may in part be that MCMC offers an appealing approach to handling some difficult types of analyses. Additionally, MCMC methods are those most commonly used for Bayesian analysis. However, epidemiologists are still largely unfamiliar with MCMC. They may lack familiarity either with the implementation of MCMC or with interpretation of the resultant output. As with tutorials outlining the calculus behind maximum likelihood in previous decades, a simple description of the machinery of MCMC is needed. We provide an introduction to conducting analyses with MCMC, and show that, given the same data and under certain model specifications, the results of an MCMC simulation match those of methods based on standard maximum-likelihood estimation (MLE). In addition, we highlight examples of instances in which MCMC approaches to data analysis provide a clear advantage over MLE. We hope that this brief tutorial will encourage epidemiologists to consider MCMC approaches as part of their analytic tool-kit.
Epidemiology; Analytical-methods; Humans; Men; Women; Models; Exposure-levels; Diseases; Mathematical-models; Magnetic-fields; Statistical-analysis
Ghassan Hamra, International Agency for Research on Cancer, 150 Cours Albert Thomas, 69008 Lyon, France
Grant-Number-R03-OH-009800; M122013; Grant-Number-T42-OH-008673
Issue of Publication
International Journal of Epidemiology
University of North Carolina at Chapel Hill
Page last reviewed: September 2, 2020
Content source: National Institute for Occupational Safety and Health Education and Information Division