Simultaneous variable selection and estimation for joint models of longitudinal and failure time data with interval censoring

Biometrics ◽  
2020 ◽  
Author(s):  
Fengting Yi ◽  
Niansheng Tang ◽  
Jianguo Sun
Keyword(s):  
Variable Selection ◽  
Failure Time ◽  
Interval Censoring ◽  
Failure Time Data ◽  
Joint Models ◽  
Time Data ◽  
Variable Selection And Estimation

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 Regression analysis of clustered interval-censored failure time data

2012 ◽  
Author(s):  
◽  
Junlong Li
Keyword(s):  
Regression Analysis ◽  
Failure Time ◽  
Interval Censoring ◽  
Maximum Likelihood Estimates ◽  
Marginal Model ◽  
Failure Time Data ◽  
Time Data ◽  
Regression Parameters ◽  
Interval Censored ◽  
Clustered Failure Time Data

Clustered failure time data occur when the failure times of interest are clustered into small groups, while interval censoring occurs when the event of interest cannot be observed directly and is only known to have occurred over a time interval. Clustered failure time data often arise together with interval-censoring, which leads to the clustered interval-censored failure time data. In this dissertation, we will focus on the regression analysis of such data. In the first part of the dissertation, a regression analysis under a Cox frailty model is discussed by employing a sieve estimation procedure. In particular, a two-step algorithm is developed for the regression parameter estimation and the asymptotic properties of the resulting sieve maximum likelihood estimates are established. The second part of this dissertation proposes an estimating equation-based approach for the additive hazards model. A major advantage of the proposed method is that it does not involve estimation of any baseline hazard function. Both asymptotic and finite sample properties of the proposed estimates of regression parameters are established and the method is illustrated by the data arising from a lymphatic filariasis study. The last part of the dissertation considers the regression analysis of the same type of data in the context of the linear transformation models. For the inference about the regression parameters, a marginal model approach based on within-cluster resampling (WCR) method is proposed and its large sample properties are also established.

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
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