scholarly journals Modeling Clustered Interval-Censored Failure Time Data with Informative Cluster Size

2014 ◽  
Vol 27 (2) ◽  
pp. 331-343 ◽  
Author(s):  
Jinheum Kim ◽  
Youn Nam Kim
Keyword(s):  
Cluster Size ◽  
Failure Time ◽  
Failure Time Data ◽  
Time Data ◽  
Interval Censored ◽  
Informative Cluster Size

Semiparametric Regression Analysis of Clustered Interval-Censored Failure Time Data with Informative Cluster Size

2013 ◽  
Vol 9 (2) ◽  
Author(s):  
Xinyan Zhang ◽  
Jianguo Sun
Keyword(s):  
Regression Analysis ◽  
Cluster Size ◽  
Failure Time ◽  
Semiparametric Regression ◽  
Estimating Equation ◽  
Parameter Estimates ◽  
Failure Time Data ◽  
Time Data ◽  
Interval Censored ◽  
Informative Cluster Size

AbstractClustered interval-censored failure time data are commonly encountered in many medical settings. In such situations, one issue that often arises in practice is that the cluster size is related to the risk for the outcome of interest. It is well-known that ignoring the informativeness of the cluster size can result in biased parameter estimates. In this article, we consider regression analysis of clustered interval-censored data with informative cluster size with the focus on semiparametric methods. For the problem, two approaches are presented and investigated. One is a within-cluster resampling procedure and the other is a weighted estimating equation approach. Unlike previously published methods, the new approaches take into account cluster sizes and heterogeneous correlation structures without imposing strong parametric assumptions. A simulation experiment is carried out to evaluate the performance of the proposed approaches and indicates that they perform well for practical situations. The approaches are applied to a lymphatic filariasis study that motivated this study.

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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