[Methodology and progress in adjusting time-dependent covariates in clinical prediction models]

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.