Bidhendi Yarandi Razieh, Mohammad Kazem, Zeraati Hojjat, Ramezani Tehrani Fahimeh, Mansournia Mohammad Ali
Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.
Reproductive Endocrinology Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Med J Islam Repub Iran. 2020 Jul 13;34:78. doi: 10.34171/mjiri.34.78. eCollection 2020.
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. 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. Unbiased estimate was obtained by the introduced methods. Bayesian and data augmentation as the advanced methods provide sufficient results and deal with most data problems such as sparsity.
贝叶斯方法在医学研究中受到了更多关注。它被视为处理科学应用问题的自然范式，也是传统频率主义方法的一种替代方法。然而，非专业人士 somewhat difficult to grasp 其概念。本研究旨在通过医学领域的一个直观示例来解释贝叶斯方法的基本思想，并举例说明贝叶斯数据分析的一些简单示例以及对贝叶斯分析得出的结果的解释。在本研究中，通过一个应用示例展示了数据稀疏性这一可以用该方法解决的问题。此外，还对贝叶斯推断进行了常识性描述，并为医学研究人员和非专业人士提供了一些有启发性的示例。介绍了数据增强先验、马尔可夫链蒙特卡罗方法（MCMC）和贝叶斯因子。来自胡齐斯坦研究（一项两阶段队列研究）的数据被用于说明。同时，研究了维生素D干预对妊娠结局的影响。通过所介绍的方法获得了无偏估计。贝叶斯方法和数据增强作为先进方法提供了充分的结果，并能处理诸如稀疏性等大多数数据问题。
