Time-varying covariates and coefficients in Cox regression models

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 propor...

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Bibliographic Details
Published in:	Annals of translational medicine Vol. 6; no. 7; p. 121
Main Authors:	Zhang, Zhongheng, Reinikainen, Jaakko, Adeleke, Kazeem Adedayo, Pieterse, Marcel E, Groothuis-Oudshoorn, Catharina G M
Format:	Journal Article
Language:	English
Published:	China AME Publishing Company 01-04-2018
Subjects:	Big-data Clinical Trial Column time-to-event Cox proportional hazards time varying Schoenfeld residuals time dependent
Online Access:	Get full text
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Summary:	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.
Bibliography:	SourceType-Other Sources-1 content type line 63 ObjectType-Editorial-2 ObjectType-Commentary-1
ISSN:	2305-5839 2305-5839
DOI:	10.21037/atm.2018.02.12