scholarly journals Use of time-varying coefficients in a Cox regression model when the proportional hazard assumption is violated

2018 ◽  
Vol 44 (11) ◽  
pp. 2017-2019 ◽  
Author(s):  
Maofeng Wang ◽  
Weimin Li ◽  
Nadir Yehya ◽  
Garrett Keim ◽  
Neal J. Thomas
Keyword(s):  
Regression Model ◽  
Cox Regression ◽  
Time Varying ◽  
Proportional Hazard ◽  
Cox Regression Model ◽  
Proportional Hazard Assumption ◽  
Varying Coefficients ◽  
Use Of Time ◽  
Time Varying Coefficients

scholarly journals Outlier robust modeling of survival curves in the presence of potentially time-varying coefficients

2020 ◽  
Vol 29 (9) ◽  
pp. 2683-2696
Author(s):  
Jorne Lionel Biccler ◽  
Martin Bøgsted ◽  
Stefan Van Aelst ◽  
Tim Verdonck
Keyword(s):  
Hazard Model ◽  
Brier Score ◽  
Model Parameters ◽  
Time Varying ◽  
Proportional Hazard ◽  
Penalty Term ◽  
Piecewise Constant ◽  
Varying Coefficients ◽  
Robust Model ◽  
Time Varying Coefficients

In time to event studies, censoring often occurs and models that take this into account are wide-spread. In the presence of outliers, standard estimators of model parameters may be affected such that results and conclusions are not reliable anymore. This in turn also hampers the detection of these outliers due to masking effects. To cope with outliers when using proportional hazard models, we propose to use the Brier score as a loss function. Since the coefficients often vary over time, we focus on the piecewise constant hazard model, which can flexibly model time-varying coefficients if a large number of cut-points is used. To prevent overfitting, we add a penalty term that potentially shrinks time-varying effects to constant effects. By fitting the coefficients of the piecewise constant hazard model using a penalized Brier score loss, we obtain a robust model that can handle time-varying coefficients. Its good performance is illustrated in a simulation study and using two datasets from practice.

scholarly journals Group MCP for Cox Models with Time-Varying Coefficients

2016 ◽  
Vol 4 (5) ◽  
pp. 476-488
Author(s):  
Xiaodong Xie ◽  
Shaozhi Zheng
Keyword(s):  
Variable Selection ◽  
Group Lasso ◽  
Partial Likelihood ◽  
Time Varying ◽  
Proportional Hazard ◽  
Cox Models ◽  
Varying Coefficients ◽  
Spline Basis ◽  
Time Varying Coefficients ◽  
Lasso Method

AbstractCox’s proportional hazard models with time-varying coefficients have much flexibility for modeling the dynamic of covariate effects. Although many variable selection procedures have been developed for Coxs proportional hazard model, the study of such models with time-varying coefficients appears to be limited. The variable selection methods involving nonconvex penalty function, such as the minimax concave penalty (MCP), introduces numerical challenge, but they still have attractive theoretical properties and were indicated that they are worth to be alternatives of other competitive methods. We propose a group MCP method that uses B-spline basis to expand coefficients and maximizes the log partial likelihood with nonconvex penalties on regression coefficients in groups. A fast, iterative group shooting algorithm is carried out for model selection and estimation. Under some appropriate conditions, the simulated example shows that our method performs competitively with the group lasso method. By comparison, the group MCP method and group lasso select the same amount of important covariates, but group MCP method tends to outperform the group lasso method in selection of unimportant covariates.

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