The power of testing a semi-parametric shared gamma frailty parameter in failure time data

2008 ◽  
Vol 27 (21) ◽  
pp. 4328-4339
Author(s):  
Mehdi Rahgozar ◽  
Soghrat Faghihzadeh ◽  
Gholamreza Babaee Rouchi ◽  
Yingwei Peng
Keyword(s):  
Failure Time ◽  
Failure Time Data ◽  
Time Data ◽  
Gamma Frailty

Assessing gamma frailty models for clustered failure time data

1995 ◽  
Vol 1 (2) ◽  
pp. 205-220 ◽  
Author(s):  
Joanna H. Shih ◽  
Thomas A. Louis
Keyword(s):  
Failure Time ◽  
Frailty Models ◽  
Failure Time Data ◽  
Time Data ◽  
Gamma Frailty ◽  
Clustered Failure Time Data

A Class of Bayesian Shared Gamma Frailty Models with Multivariate Failure Time Data

Biometrics ◽  
2005 ◽  
Vol 61 (1) ◽  
pp. 208-216 ◽  
Author(s):  
Guosheng Yin ◽  
Joseph G. Ibrahim
Keyword(s):  
Failure Time ◽  
Frailty Models ◽  
Failure Time Data ◽  
Time Data ◽  
Multivariate Failure Time ◽  
Gamma Frailty

Correlation analysis of software failure time data

2010 ◽  
Vol 30 (3) ◽  
pp. 600-602
Author(s):  
Jun-gang LOU ◽  
Jian-hui JIANG
Keyword(s):  
Correlation Analysis ◽  
Failure Time ◽  
Failure Time Data ◽  
Time Data ◽  
Software Failure

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.

Regression analysis of interval-censored failure time data with possibly crossing hazards

2017 ◽  
Vol 37 (5) ◽  
pp. 768-775 ◽  
Author(s):  
Han Zhang ◽  
Peijie Wang ◽  
Jianguo Sun
Keyword(s):  
Regression Analysis ◽  
Failure Time ◽  
Failure Time Data ◽  
Time Data ◽  
Interval Censored ◽  
Crossing Hazards
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