Cox Regression Models with Functional Covariates for Survival Data.
Cox Regression Models with Functional Covariates for Survival Data.
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DOI：
10.1177/1471082x14565526
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发表时间：
2015-06-01
影响因子：
1
通讯作者：
Crainiceanu CM
中科院分区：
文献类型：
--
作者：
Gellar JE;Colantuoni E;Needham DM;Crainiceanu CM
我们将Cox比例风险模型扩展到暴露是一个密集采样的功能过程，在基线测量的情况下。基本思想是将惩罚信号回归与混合效应比例风险模型的方法相结合。该模型通过最大化惩罚的部分似然来拟合，平滑参数由基于似然的准则（如AIC或EPIC）估计。该模型可以扩展到允许多个功能预测器、时变系数和缺失或不等间隔的数据。方法的灵感来自于急性呼吸窘迫综合征幸存者出院后死亡时间与重症监护病房收集的疾病严重程度的日常测量之间的关系的研究。
We extend the Cox proportional hazards model to cases when the exposure is a densely sampled functional process, measured at baseline. The fundamental idea is to combine penalized signal regression with methods developed for mixed effects proportional hazards models. The model is fit by maximizing the penalized partial likelihood, with smoothing parameters estimated by a likelihood-based criterion such as AIC or EPIC. The model may be extended to allow for multiple functional predictors, time varying coefficients, and missing or unequally-spaced data. Methods were inspired by and applied to a study of the association between time to death after hospital discharge and daily measures of disease severity collected in the intensive care unit, among survivors of acute respiratory distress syndrome.