McGlothlin Anna, Stamey James D, Seaman John W
Exploratory Program Medical Statistics, Eli Lilly and Company, Lilly Corporate Center DC 0710, Indianapolis, IN 46285, USA.
Biom J. 2008 Feb;50(1):123-34. doi: 10.1002/bimj.200710402.
We consider a Bayesian analysis for modeling a binary response that is subject to misclassification. Additionally, an explanatory variable is assumed to be unobservable, but measurements are available on its surrogate. A binary regression model is developed to incorporate the measurement error in the covariate as well as the misclassification in the response. Unlike existing methods, no model parameters need be assumed known. Markov chain Monte Carlo methods are utilized to perform the necessary computations. The methods developed are illustrated using atomic bomb survival data. A simulation experiment explores advantages of the approach.
我们考虑对存在误分类的二元响应进行贝叶斯分析建模。此外，假设一个解释变量不可观测，但可获得其替代变量的测量值。我们开发了一个二元回归模型，以纳入协变量中的测量误差以及响应中的误分类。与现有方法不同，无需假设任何模型参数已知。利用马尔可夫链蒙特卡罗方法进行必要的计算。使用原子弹幸存者数据对所开发的方法进行了说明。一个模拟实验探讨了该方法的优势。
