Dynamic prediction of competing risk events using landmark sub-distribution hazard model with multiple longitudinal biomarkers.
Dynamic prediction of competing risk events using landmark sub-distribution hazard model with multiple longitudinal biomarkers.
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DOI：
10.1177/0962280220921553
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发表时间：
2020-11
影响因子：
2.3
通讯作者：
中科院分区：
文献类型：
--
作者：
病因特异性累积发生率函数（CIF）量化了具有竞争性风险结局的受试者特异性疾病风险。利用纵向收集的生物标志物数据，感兴趣的是通过结合最近的生物标志物以及累积的纵向历史来动态更新预测的CIF。受慢性肾脏疾病纵向队列研究的启发，我们提出了一个使用多变量纵向生物标志物动态预测终末期肾脏疾病的框架，考虑了死亡的竞争风险。所提出的框架将基于局部估计的地标生存建模扩展到竞争风险数据，并意味着在每个生物标志物测量时间定义不同的子分布风险回归模型。允许模型参数、预测范围、纵向历史和风险人群在里程碑时间内变化。当生物标志物的测量时间间隔不规则时，在预测时可能无法观察到预测变量。局部多项式被用来估计模型参数，而不显式地插补预测或建模其纵向轨迹。所提出的模型导致简单的解释的回归系数和封闭式计算的预测CIF。估计和预测可以通过具有易处理计算的标准统计软件来实现。我们进行了模拟评估的估计过程和预测精度的性能。该方法说明了从非裔美国人的肾脏疾病和高血压研究的数据。
The cause-specific cumulative incidence function (CIF) quantifies the subject-specific disease risk with competing risk outcome. With longitudinally collected biomarker data, it is of interest to dynamically update the predicted CIF by incorporating the most recent biomarker as well as the cumulating longitudinal history. Motivated by a longitudinal cohort study of chronic kidney disease, we propose a framework for dynamic prediction of end stage renal disease using multivariate longitudinal biomarkers, accounting for the competing risk of death. The proposed framework extends the local estimation based landmark survival modeling to competing risks data, and implies that a distinct sub-distribution hazard regression model is defined at each biomarker measurement time. The model parameters, prediction horizon, longitudinal history and at-risk population are allowed to vary over the landmark time. When the measurement times of biomarkers are irregularly spaced, the predictor variable may not be observed at the time of prediction. Local polynomial is used to estimate the model parameters without explicitly imputing the predictor or modeling its longitudinal trajectory. The proposed model leads to simple interpretation of the regression coefficients and closed-form calculation of the predicted CIF. The estimation and prediction can be implemented through standard statistical software with tractable computation. We conducted simulations to evaluate the performance of the estimation procedure and predictive accuracy. The methodology is illustrated with data from the African American Study of Kidney Disease and Hypertension.
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影响因子：
1
作者：
Li L;Luo S;Hu B;Greene T
通讯作者：
Greene T
影响因子：
2.3
作者：
Andrinopoulou, Eleni-Rosalina;Rizopoulos, D.;Lesaffre, E.
通讯作者：
Lesaffre, E.
影响因子：
1.9
作者：
Geskus, Ronald B.
通讯作者：
Geskus, Ronald B.
DOI：
10.1097/01.asn.0000070081.15137.c0
发表时间：
2003-07-01
影响因子：
13.6
作者：
Appel, LJ;Middleton, J;Wright, JT
通讯作者：
Wright, JT
影响因子：
6.1
作者：
Noordzij, Marlies;Leffondre, Karen;Jager, Kitty J.
通讯作者：
Jager, Kitty J.