Time-varying covariates and coefficients in Cox regression models.

By Wentao Bao, J. Reinikainen, Kazeem Adedayo Adeleke, Marcel E. Pieterse, C. Groothuis-Oudshoorn. Annals of Translational Medicine, 2018

687 citations recorded by the source corpus.

Time-varying covariance occurs when a covariate changes over time during the follow-up period. Such variable can be analyzed with the Cox regression model to estimate its effect on survival time. For this it is essential to organize the data in a counting process style. In situations when the proportional hazards assumption of the Cox regression model does not hold, we say that the effect of the covariate is time-varying. The proportional hazards assumption can be tested by examining the residuals of the model. The rejection of the null hypothesis induces the use of time varying coefficient to describe the data. The time varying coefficient can be described with a step function or a parametric time function. This article aims to illustrate how to carry out statistical analyses in the presence of time-varying covariates or coefficients with R.

Subfields: Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/2ff1725766e2e06d5f2a09936fc18dae20c7d202

Citations in the corpus

• Real-time prediction of COVID-19 related mortality using electronic health records (2020, neutral): “[15] Zhongheng Zhang, Jaakko Reinikainen, Kazeem Adedayo Adeleke, Marcel E Pieterse, and Catharina GM Groothuis-Oudshoorn.”