Semiparametric transformation models for interval-censored data in the presence of a cure fraction

2018 ◽  
Vol 61 (1) ◽  
pp. 203-215 ◽  
Author(s):  
Chyong-Mei Chen ◽  
Pao-sheng Shen ◽  
Wei-Lun Huang
Keyword(s):  
Censored Data ◽  
Interval Censored Data ◽  
Transformation Models ◽  
Cure Fraction ◽  
Interval Censored ◽  
Semiparametric Transformation

Penalized estimation of semiparametric transformation models with interval-censored data and application to Alzheimer’s disease

2019 ◽  
Vol 29 (8) ◽  
pp. 2151-2166 ◽  
Author(s):  
Shuwei Li ◽  
Qiwei Wu ◽  
Jianguo Sun
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Variable Selection ◽  
Censored Data ◽  
Interval Censoring ◽  
Interval Censored Data ◽  
Transformation Models ◽  
Interval Censored ◽  
Semiparametric Transformation ◽  
Disease Study

Variable selection or feature extraction is fundamental to identify important risk factors from a large number of covariates and has applications in many fields. In particular, its applications in failure time data analysis have been recognized and many methods have been proposed for right-censored data. However, developing relevant methods for variable selection becomes more challenging when one confronts interval censoring that often occurs in practice. In this article, motivated by an Alzheimer’s disease study, we develop a variable selection method for interval-censored data with a general class of semiparametric transformation models. Specifically, a novel penalized expectation–maximization algorithm is developed to maximize the complex penalized likelihood function, which is shown to perform well in the finite-sample situation through a simulation study. The proposed methodology is then applied to the interval-censored data arising from the Alzheimer’s disease study mentioned above.

Linear transformation models for interval-censored data

2009 ◽  
Vol 9 (4) ◽  
pp. 321-343 ◽  
Author(s):  
Zhigang Zhang
Keyword(s):  
Statistical Analysis ◽  
Censored Data ◽  
Linear Transformation ◽  
Graphical Model ◽  
Real Life ◽  
Random Variable ◽  
Interval Censored Data ◽  
Transformation Models ◽  
Linear Transformation Models ◽  
Interval Censored

In statistical analysis, when the value of a random variable is only known to be between two bounds, we say that this random variable is interval censored. This complicated censoring pattern is a common problem in research fields such as clinical trials or actuarial studies and raises challenges for statistical analysis. In this paper, we focus on regression analysis of case 2 interval-censored data. We first briefly review existing regression methods and an estimation approach under the class of linear transformation models developed by Zhang et al. We then propose a method for survival probability prediction via generalized estimating equations. We also consider a graphical model checking technique and a model selection tool. Some theoretical properties are established and the performance of our procedures is evaluated and illustrated by numerical studies including a real-life data analysis.

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