Variable selection for joint models of multivariate longitudinal measurements and event time data

2017 ◽  
Vol 36 (24) ◽  
pp. 3820-3829 ◽  
Author(s):  
Yuqi Chen ◽  
Yuedong Wang
Keyword(s):  
Variable Selection ◽  
Joint Models ◽  
Time Data ◽  
Event Time ◽  
Longitudinal Measurements ◽  
Selection For

scholarly journals Simultaneous variable selection for joint models of longitudinal and survival outcomes

Biometrics ◽  
2014 ◽  
Vol 71 (1) ◽  
pp. 178-187 ◽  
Author(s):  
Zangdong He ◽  
Wanzhu Tu ◽  
Sijian Wang ◽  
Haoda Fu ◽  
Zhangsheng Yu
Keyword(s):  
Variable Selection ◽  
Survival Outcomes ◽  
Joint Models ◽  
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.

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.

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