Partly conditional survival models for longitudinal data
Partly conditional survival models for longitudinal data
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DOI：
10.1111/j.1541-0420.2005.00323.x
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发表时间：
2005-06-01
期刊：
影响因子：
1.9
通讯作者：
Heagerty, PJ
中科院分区：
文献类型：
--
作者：
Zheng, YY;Heagerty, PJ
在纵向研究中，通常收集关键临床事件（例如死亡）之前的时间信息，并在多个随访时间测量患者健康指标。联合分析生存和重复测量数据的一种方法采用事件时间风险的时变协变量回归模型。使用这种标准方法，时间 t 时的瞬时死亡风险被指定为通过时间 t 累积的协变量信息的可能半参数函数。在这份手稿中，我们将风险建模的时间尺度与可用纵向协变量信息的累积时间尺度解耦。具体来说，我们提出了一类模型，以时间 s 内的协变量信息为条件，然后指定时间 t 的条件风险，其中 t > s。我们的方法与 Pepe 和 Couper (1997, Journal of the American Statistical Association 92, 991-998) 提出的用于纯重复测量应用的“部分条件”模型类似。估计基于使用估计方程应用于通过创建派生生存时间形成的数据簇，这些生存时间测量从协变量测量到随访结束的时间。患者随访可能因事件的发生或审查而终止。所提出的方法允许灵活地表征纵向协变量过程和生存时间之间的关联，并有助于直接预测时变协变量设置中的生存概率。
It is common in longitudinal studies to collect information on the time until a key clinical event, such as death, and to measure markers of patient health at multiple follow-up times. One approach to the joint analysis of survival and repeated measures data adopts a time-varying covariate regression model for the event time hazard. Using this standard approach, the instantaneous risk of death at time t is specified as a possibly semiparametric function of covariate information that has accrued through time t. In this manuscript, we decouple the time scale for modeling the hazard from the time scale for accrual of available longitudinal covariate information. Specifically, we propose a class of models that condition on the covariate information through time s and then specifies the conditional hazard for times t, where t > s. Our approach parallels the "partly conditional" models proposed by Pepe and Couper (1997, Journal of the American Statistical Association 92, 991-998) for pure repeated measures applications. Estimation is based on the use of estimating equations applied to clusters of data formed through the creation of derived survival times that measure the time from measurement of covariates to the end of follow-up. Patient follow-up may be terminated either by the occurrence of the event or by censoring. The proposed methods allow a flexible characterization of the association between a longitudinal covariate process and a survival time, and facilitate the direct prediction of survival probabilities in the time-varying covariate setting.