Vidal Ignacio, de Castro Mário
a Instituto de Matemática y Física, Universidad de Talca , Talca , Chile.
b Universidade de São Paulo, Instituto de Ciências Matemáticas e de Computação , São Carlos , SP , Brasil.
J Biopharm Stat. 2017;27(5):809-823. doi: 10.1080/10543406.2016.1226323. Epub 2016 Nov 1.
The agreement of different measurement methods is an important issue in several disciplines like, for example, Medicine, Metrology, and Engineering. In this article, some agreement measures, common in the literature, were analyzed from a Bayesian point of view. Posterior inferences for such agreement measures were obtained based on well-known Bayesian inference procedures for the bivariate normal distribution. As a consequence, a general, simple, and effective method is presented, which does not require Markov Chain Monte Carlo methods and can be applied considering a great variety of prior distributions. Illustratively, the method was exemplified using five objective priors for the bivariate normal distribution. A tool for assessing the adequacy of the model is discussed. Results from a simulation study and an application to a real dataset are also reported.
不同测量方法之间的一致性是医学、计量学和工程学等多个学科中的一个重要问题。在本文中，从贝叶斯的角度分析了文献中常见的一些一致性度量。基于双变量正态分布的著名贝叶斯推断程序，获得了此类一致性度量的后验推断。因此，提出了一种通用、简单且有效的方法，该方法不需要马尔可夫链蒙特卡罗方法，并且可以考虑各种先验分布来应用。作为示例，使用双变量正态分布的五个客观先验对该方法进行了举例说明。讨论了一种评估模型适用性的工具。还报告了模拟研究的结果以及对真实数据集的应用。
