A joint model for longitudinal measurements and survival data in the presence of multiple failure types
A joint model for longitudinal measurements and survival data in the presence of multiple failure types
复制标题
DOI：
10.1111/j.1541-0420.2007.00952.x
复制
发表时间：
2008-09-01
期刊：
影响因子：
1.9
通讯作者：
Li, Ning
中科院分区：
文献类型：
--
作者：
Elashoff, Robert M.;Li, Gang;Li, Ning
在这篇文章中，我们研究了纵向测量和竞争风险生存数据的联合模型。我们的联合模型提供了一个灵活的方法来处理可能的不可重复的丢失数据的纵向测量由于辍学。它也是以前的联合模型的扩展，具有单一的故障类型，提供了一种可能的方法来建模信息删失事件作为竞争风险。我们的模型由纵向结果的线性混合效应子模型和比例原因特异性危险脆弱子模型组成（普伦蒂斯等人，1978，Biometrics 34，541-554），用于通过一些潜在的随机效应连接在一起的竞争风险生存数据。我们建议通过期望最大化（EM）算法获得参数的最大似然估计，并使用轮廓似然方法估计其标准误差。所开发的方法在我们的模拟研究中效果良好，并应用于硬皮病肺病的临床试验。
In this article we study a joint model for longitudinal measurements and competing risks survival data. Our joint model provides a flexible approach to handle possible nonignorable missing data in the longitudinal measurements due to dropout. It is also an extension of previous joint models with a single failure type, offering a possible way to model informatively censored events as a competing risk. Our model consists of a linear mixed effects submodel for the longitudinal outcome and a proportional cause-specific hazards frailty submodel (Prentice et al., 1978, Biometrics 34, 541-554) for the competing risks survival data, linked together by some latent random effects. We propose to obtain the maximum likelihood estimates of the parameters by an expectation maximization (EM) algorithm and estimate their standard errors using a profile likelihood method. The developed method works well in our simulation studies and is applied to a clinical trial for the scleroderma lung disease.