RT - Journal Article T1 - Bayesian methods for clinicians JF - MJIRI YR - 2020 JO - MJIRI VO - 34 IS - 1 UR - http://mjiri.iums.ac.ir/article-1-6079-en.html SP - 548 EP - 553 K1 - Bayesian inference K1 - Prior information K1 - MCMC K1 - Data augmentation AB - Background: The Bayesian methods have received more attention in medical research. It is considered as a natural paradigm for dealing with applied problems in the sciences and also an alternative to the traditional frequentist approach. However, its concept is somewhat difficult to grasp by nonexperts. This study aimed to explain the foundational ideas of the Bayesian methods through an intuitive example in medical science and to illustrate some simple examples of Bayesian data analysis and the interpretation of results delivered by Bayesian analyses. In this study, data sparsity, as a problem which could be solved by this approach, was presented through an applied example. Moreover, a common sense description of Bayesian inference was offered and some illuminating examples were provided for medical investigators and nonexperts. Methods: Data augmentation prior, MCMC, and Bayes factor were introduced. Data from the Khuzestan study, a 2-phase cohort study, were applied for illustration. Also, the effect of vitamin D intervention on pregnancy outcomes was studied. Results: Unbiased estimate was obtained by the introduced methods. Conclusion: Bayesian and data augmentation as the advanced methods provide sufficient results and deal with most data problems such as sparsity. LA eng UL http://mjiri.iums.ac.ir/article-1-6079-en.html M3 10.47176/mjiri.34.78 ER -