用于生存数据的具有函数协变量的Cox回归模型
Cox Regression Models with Functional Covariates for Survival Data.
作者信息
Gellar Jonathan E, Colantuoni Elizabeth, Needham Dale M, Crainiceanu Ciprian M
机构信息
Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA.
Pulmonary & Critical Care Medicine, and Physical Medicine & Rehabilitation, School of Medicine, Johns Hopkins University, Baltimore, MD, USA.
出版信息
Stat Modelling. 2015 Jun 1;15(3):256-278. doi: 10.1177/1471082X14565526. Epub 2015 Jan 9.
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.
我们将Cox比例风险模型扩展到暴露为在基线时密集采样的函数过程的情况。基本思想是将惩罚信号回归与为混合效应比例风险模型开发的方法相结合。通过最大化惩罚偏似然来拟合模型，平滑参数通过基于似然的准则（如AIC或EPIC）进行估计。该模型可以扩展到允许多个函数预测变量、时变系数以及缺失或不等距数据。这些方法的灵感来源于一项关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每日测量值之间关联的研究，并应用于该研究。
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