Dynamic risk score modeling for multiple longitudinal risk factors and survival

被引:2
作者
Zhang, Cuihong [1 ]
Ning, Jing [2 ]
Cai, Jianwen [3 ]
Squires, James E. [4 ]
Belle, Steven H. [5 ]
Li, Ruosha [1 ]
机构
[1] Univ Texas Hlth Sci Ctr Houston, Dept Biostat & Data Sci, Houston, TX 77479 USA
[2] Univ Texas MD Anderson Canc Ctr, Dept Biostat, Houston, TX USA
[3] Univ North Carolina Chapel Hill, Dept Biostat, Chapel Hill, NC USA
[4] UPMC Childrens Hosp Pittsburgh, Div Gastroenterol Hepatol & Nutr, Pittsburgh, PA USA
[5] Univ Pittsburgh, Dept Psychiat, Pittsburgh, PA USA
基金
美国国家卫生研究院;
关键词
Time-dependent covariates; Longitudinal risk score; Dynamic prediction; Competing risks; Joint model; Pediatric acute liver failure; TIME-TO-EVENT; COMPETING RISKS; JOINT ANALYSIS; PROGNOSIS; FAILURE; SYSTEM; DEATH;
D O I
10.1016/j.csda.2023.107837
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Modeling disease risk and survival using longitudinal risk factor trajectories is of interest in various clinical scenarios. The capacity to build a prognostic model using the trajectories of multiple longitudinal risk factors, in the presence of potential dependent censoring, would enable more informed, personalized decision making. A dynamic risk score modeling framework is proposed for multiple longitudinal risk factors and survival in the presence of dependent censoring, where both events depend on participants' post-baseline clinical progression and form a competing risks structure. The model requires relatively few random effects regardless of the number of longitudinal risk factors and can therefore accommodate multiple longitudinal risk factors in a parsimonious manner. The proposed method performed satisfactorily in extensive simulation studies. It is further applied to the motivating registry study on pediatric acute liver failure to model death using the trajectories of multiple clinical and biochemical markers. Once established, the model yields an easily calculable longitudinal risk score that can be used for disease monitoring among future patients.& COPY; 2023 Elsevier B.V. All rights reserved.
引用
收藏
页数:12
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