Smooth Goodness-of-Fit Tests for the Baseline Hazard in Cox's Proportional Hazards Model

1998 ◽  
Vol 93 (442) ◽  
pp. 673 ◽  
Author(s):  
Edsel A. Pena
Keyword(s):  
Goodness Of Fit ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Baseline Hazard ◽  
Goodness Of Fit Tests ◽  
Hazards Model ◽  
Cox's Proportional Hazards Model

A Simple Test for Goodness of Fit of Cox's Proportional Hazards Model

Biometrics ◽  
1984 ◽  
Vol 40 (2) ◽  
pp. 483 ◽  
Author(s):  
N. J. D. Nagelkerke ◽  
J. Oosting ◽  
A. A. M. Hart
Keyword(s):  
Goodness Of Fit ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Simple Test ◽  
Hazards Model ◽  
Cox's Proportional Hazards Model

A unified approach to variable selection for Cox’s proportional hazards model with interval-censored failure time data

2021 ◽  
pp. 096228022110092
Author(s):  
Mingyue Du ◽  
Hui Zhao ◽  
Jianguo Sun
Keyword(s):  
Variable Selection ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Failure Time ◽  
Significant Feature ◽  
Failure Time Data ◽  
Time Data ◽  
Hazards Model ◽  
Interval Censored ◽  
Cox's Proportional Hazards Model

Cox’s proportional hazards model is the most commonly used model for regression analysis of failure time data and some methods have been developed for its variable selection under different situations. In this paper, we consider a general type of failure time data, case K interval-censored data, that include all of other types discussed as special cases, and propose a unified penalized variable selection procedure. In addition to its generality, another significant feature of the proposed approach is that unlike all of the existing variable selection methods for failure time data, the proposed approach allows dependent censoring, which can occur quite often and could lead to biased or misleading conclusions if not taken into account. For the implementation, a coordinate descent algorithm is developed and the oracle property of the proposed method is established. The numerical studies indicate that the proposed approach works well for practical situations and it is applied to a set of real data arising from Alzheimer’s Disease Neuroimaging Initiative study that motivated this study.

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