论文标题
通过随机效应在受试者水平上与空间相关的随机效应来建模依赖的生存数据
Modeling dependent survival data through random effects with spatial correlation at the subject level
论文作者
论文摘要
经常通过纵向跟踪受试者，从而为事件数据产生时间来研究动力现象，例如传染病。此类数据的空间方面也具有原始的重要性，因为许多传染病都从一个主题传播到另一个受试者。在本文中，引入了一个与空间相关的脆弱模型，该模型可适应受试者之间基于它们之间的距离之间的相关性。估计是通过与蒙特卡洛马尔可夫链结合的期望最大化算法的随机近似版本获得的，该算法被证明了收敛性。该模型的新颖性是在主题级别引入了空间相关性以用于生存数据，每个受试者都有自己的脆弱性。这种单变量相关的脆弱模型用于分析空间依赖的疟疾数据，并将其结果与其他标准模型进行比较。
Dynamical phenomena such as infectious diseases are often investigated by following up subjects longitudinally, thus generating time to event data. The spatial aspect of such data is also of primordial importance, as many infectious diseases are transmitted from one subject to another. In this paper, a spatially correlated frailty model is introduced that accommodates for the correlation between subjects based on the distance between them. Estimates are obtained through a stochastic approximation version of the Expectation Maximization algorithm combined with a Monte-Carlo Markov Chain, for which convergence is proven. The novelty of this model is that spatial correlation is introduced for survival data at the subject level, each subject having its own frailty. This univariate spatially correlated frailty model is used to analyze spatially dependent malaria data, and its results are compared with other standard models.
