Variable selection for generalized odds rate mixture cure models with interval-censored failure time data

2021 ◽  
Vol 156 ◽  
pp. 107115
Author(s):  
Yang Xu ◽  
Shishun Zhao ◽  
Tao Hu ◽  
Jianguo Sun
Keyword(s):  
Variable Selection ◽  
Failure Time ◽  
Failure Time Data ◽  
Time Data ◽  
Cure Models ◽  
Interval Censored ◽  
Selection For

Variable selection for interval-censored failure time data

2019 ◽  
Author(s):  
◽  
Qiwei Wu
Keyword(s):  
Variable Selection ◽  
Failure Time ◽  
Subset Selection ◽  
Failure Time Data ◽  
Time Data ◽  
University Of Missouri ◽  
Interval Censored ◽  
Selection For ◽  
The University

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] Variable selection is a commonly asked question and various traditional variable selec- tion methods have been developed, including forward, backward and stepwise selec- tion, as well as best subset selection. Among these conventional selection techniques, the best subset selection is recommended by most researchers. However, this method requires fitting all sub-models, which can be very time-consuming when the number of covariates p is large.

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.

scholarly journals Nonparametric screening and feature selection for ultrahigh‐dimensional Case II interval‐censored failure time data

2020 ◽  
Vol 62 (8) ◽  
pp. 1909-1925
Author(s):  
Qiang Hu ◽  
Liang Zhu ◽  
Yanyan Liu ◽  
Jianguo Sun ◽  
Deo Kumar Srivastava ◽  
...  
Keyword(s):  
Feature Selection ◽  
Failure Time ◽  
Dimensional Case ◽  
Failure Time Data ◽  
Time Data ◽  
Interval Censored ◽  
Selection For

scholarly journals Variable selection for multivariate failure time data

Biometrika ◽  
2005 ◽  
Vol 92 (2) ◽  
pp. 303-316 ◽  
Author(s):  
Jianwen Cai ◽  
Jianqing Fan ◽  
Runze Li ◽  
Haibo Zhou
Keyword(s):  
Variable Selection ◽  
Failure Time ◽  
Failure Time Data ◽  
Time Data ◽  
Multivariate Failure Time ◽  
Selection For

scholarly journals Variable selection for mixture and promotion time cure rate models

2016 ◽  
Vol 27 (7) ◽  
pp. 2185-2199 ◽  
Author(s):  
Abdullah Masud ◽  
Wanzhu Tu ◽  
Zhangsheng Yu
Keyword(s):  
Variable Selection ◽  
Failure Time ◽  
Operating Characteristics ◽  
Cure Rate ◽  
Time Data ◽  
Cure Rate Models ◽  
Cure Models ◽  
Selection Operators ◽  
Selection For ◽  
Using Data

Failure-time data with cured patients are common in clinical studies. Data from these studies are typically analyzed with cure rate models. Variable selection methods have not been well developed for cure rate models. In this research, we propose two least absolute shrinkage and selection operators based methods, for variable selection in mixture and promotion time cure models with parametric or nonparametric baseline hazards. We conduct an extensive simulation study to assess the operating characteristics of the proposed methods. We illustrate the use of the methods using data from a study of childhood wheezing.

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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