Influence analysis for skew-normal semiparametric joint models of multivariate longitudinal and multivariate survival data
Influence analysis for skew-normal semiparametric joint models of multivariate longitudinal and multivariate survival data
复制标题
多元纵向和多元生存数据的偏态正态半参数联合模型的影响分析
DOI：
10.1002/sim.7211
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发表时间：
2017
影响因子：
2
通讯作者：
Hongtu Zhu
中科院分区：
文献类型：
--
作者：
An-Min Tang;Nian-Sheng Tang;Hongtu Zhu
测量误差的正态性假设是纵向和生存数据联合模型中广泛使用的一种分布，但当纵向数据呈现偏态特征时，可能会导致不合理甚至误导的结果。本文提出了一种新的多变量纵向和多变量生存数据联合模型，该模型在轨迹函数和危险函数中加入非参数函数，并假设纵向测量模型中的测量误差服从斜正态分布。蒙特卡罗期望最大化（EM）算法与惩罚样条技术和吉布斯采样器中的Metropolis-Hastings算法一起开发，用于估计所考虑的联合模型中的参数和非参数函数。提出了病例删除诊断方法来识别潜在的影响观测值，并提出了一种扩展的局部影响方法来评估小扰动的局部影响。模拟研究和临床试验的一个真实例子来说明所提出的方法。版权所有©2017 John Wiley & Sons， Ltd
The normality assumption of measurement error is a widely used distribution in joint models of longitudinal and survival data, but it may lead to unreasonable or even misleading results when longitudinal data reveal skewness feature. This paper proposes a new joint model for multivariate longitudinal and multivariate survival data by incorporating a nonparametric function into the trajectory function and hazard function and assuming that measurement errors in longitudinal measurement models follow a skew‐normal distribution. A Monte Carlo Expectation‐Maximization (EM) algorithm together with the penalized‐splines technique and the Metropolis–Hastings algorithm within the Gibbs sampler is developed to estimate parameters and nonparametric functions in the considered joint models. Case deletion diagnostic measures are proposed to identify the potential influential observations, and an extended local influence method is presented to assess local influence of minor perturbations. Simulation studies and a real example from a clinical trial are presented to illustrate the proposed methodologies. Copyright © 2017 John Wiley & Sons, Ltd.