Models for longitudinal data with censored changepoints
Models for longitudinal data with censored changepoints
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DOI：
10.1046/j.0035-9254.2003.05116.x
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发表时间：
2004-01-01
影响因子：
1.6
通讯作者：
Sharples, LD
中科院分区：
文献类型：
--
作者：
Jackson, CH;Sharples, LD
在生物标志物的纵向研究中，不同的个体可能有不同的潜在反应模式。在某些应用中，个体的子集经历潜在事件，导致标记轨迹的水平或斜率的瞬时变化。本文提出了一系列生物标志物的层次纵向模型的一般混合。兴趣集中在事件发生的时间和事件前后生物标志物的水平上。在观察性研究中，标记序列是不完整的，潜在事件可以通过生存分布来建模。事件发生的危险因素可以通过纳入生存分布中的协变量来调查。采用Gibbs、Metropolis-Hastings和可逆跳跃马尔可夫链蒙特卡罗采样相结合的方法，拟合肺移植受者用力呼气量的连续测量结果。
In longitudinal studies of biological markers, different individuals may have different underlying patterns of response. In some applications, a subset of individuals experiences latent events, causing an instantaneous change in the level or slope of the marker trajectory. The paper presents a general mixture of hierarchical longitudinal models for serial biomarkers. Interest centres both on the time of the event and on levels of the biomarker before and after the event. In observational studies where marker series are incomplete, the latent event can be modelled by a survival distribution. Risk factors for the occurrence of the event can be investigated by including covariates in the survival distribution. A combination of Gibbs, Metropolis-Hastings and reversible jump Markov chain Monte Carlo sampling is used to fit the models to serial measurements of forced expiratory volume from lung transplant recipients.