Zhu Hongtu, Ibrahim Joseph G, Chi Yueh-Yun, Tang Niansheng
Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599-7420, USA.
Biometrics. 2012 Sep;68(3):954-64. doi: 10.1111/j.1541-0420.2012.01745.x. Epub 2012 Mar 4.
This article develops a variety of influence measures for carrying out perturbation (or sensitivity) analysis to joint models of longitudinal and survival data (JMLS) in Bayesian analysis. A perturbation model is introduced to characterize individual and global perturbations to the three components of a Bayesian model, including the data points, the prior distribution, and the sampling distribution. Local influence measures are proposed to quantify the degree of these perturbations to the JMLS. The proposed methods allow the detection of outliers or influential observations and the assessment of the sensitivity of inferences to various unverifiable assumptions on the Bayesian analysis of JMLS. Simulation studies and a real data set are used to highlight the broad spectrum of applications for our Bayesian influence methods.
本文针对贝叶斯分析中的纵向和生存数据联合模型（JMLS），开发了多种用于进行扰动（或敏感性）分析的影响度量。引入了一个扰动模型来刻画对贝叶斯模型三个组成部分的个体和全局扰动，这三个组成部分包括数据点、先验分布和抽样分布。提出了局部影响度量来量化对JMLS的这些扰动程度。所提出的方法能够检测异常值或有影响的观测值，并评估对JMLS贝叶斯分析中各种不可验证假设的推断敏感性。通过模拟研究和一个实际数据集来突出我们的贝叶斯影响方法的广泛应用。
