Methodology and progress in adjusting time-dependent covariates in clinical prediction models
10.3760/cma.j.cn112338-20230128-00042
- VernacularTitle:临床预测模型中处理时依性变量的策略及进展
- Author:
Yuelin YU
1
;
Yang XU
;
Junfeng WANG
;
Siyan ZHAN
;
Shengfeng WANG
Author Information
1. 北京大学公共卫生学院流行病与卫生统计学系/重大疾病流行病学教育部重点实验室，北京 100191
- Keywords:
Clinical prediction model;
Time-dependent covariate;
Dynamic prediction;
Machine learning
- From:
Chinese Journal of Epidemiology
2023;44(8):1316-1320
- CountryChina
- Language:Chinese
-
Abstract:
Adjusting time-dependent covariates into prediction models may help improve model performance and expand clinical applications. The methodology of handling time-dependent covariates is limited in traditional regression strategies (i.e., landmark model, joint model). For example, the number of predictors and practical situations which can be handled are restricted when using regression models. One new strategy is to use machine learning (i.e., neural networks). This review summarizes the methodology of handling time-dependent covariates in prediction models, such as applicable scenarios, strengths, and limitations, to offer methodological enlightenment for processing time-dependent covariates.
