scholarly journals Variable selection in a flexible parametric mixture cure model with interval‐censored data

2015 ◽  
Vol 35 (7) ◽  
pp. 1210-1225 ◽  
Author(s):  
Sylvie Scolas ◽  
Anouar El Ghouch ◽  
Catherine Legrand ◽  
Abderrahim Oulhaj
Keyword(s):  
Variable Selection ◽  
Censored Data ◽  
Interval Censored Data ◽  
Cure Model ◽  
Mixture Cure Model ◽  
Interval Censored

A semiparametric mixture model approach for regression analysis of partly interval-censored data with a cured subgroup

2021 ◽  
pp. 096228022110239
Author(s):  
Liuquan Sun ◽  
Shuwei Li ◽  
Lianming Wang ◽  
Xinyuan Song
Keyword(s):  
Censored Data ◽  
Failure Time ◽  
Interval Censoring ◽  
Childhood Mortality ◽  
Interval Censored Data ◽  
Finite Sample ◽  
Cure Model ◽  
Mixture Cure Model ◽  
Interval Censored ◽  
Model Approach

Failure time data with a cured subgroup are frequently confronted in various scientific fields and many methods have been proposed for their analysis under right or interval censoring. However, a cure model approach does not seem to exist in the analysis of partly interval-censored data, which consist of both exactly observed and interval-censored observations on the failure time of interest. In this article, we propose a two-component mixture cure model approach for analyzing such type of data. We employ a logistic model to describe the cured probability and a proportional hazards model to model the latent failure time distribution for uncured subjects. We consider maximum likelihood estimation and develop a new expectation-maximization algorithm for its implementation. The asymptotic properties of the resulting estimators are established and the finite sample performance of the proposed method is examined through simulation studies. An application to a set of real data on childhood mortality in Nigeria is provided.

scholarly journals A semiparametric cure model for interval-censored data

2013 ◽  
Vol 55 (5) ◽  
pp. 771-788 ◽  
Author(s):  
Kwok Fai Lam ◽  
Kin Yau Wong ◽  
Feifei Zhou
Keyword(s):  
Censored Data ◽  
Interval Censored Data ◽  
Cure Model ◽  
Interval Censored

Simultaneous variable selection in regression analysis of multivariate interval‐censored data

Biometrics ◽  
2021 ◽  
Author(s):  
Liuquan Sun ◽  
Shuwei Li ◽  
Lianming Wang ◽  
Xinyuan Song ◽  
Xuemei Sui
Keyword(s):  
Regression Analysis ◽  
Variable Selection ◽  
Censored Data ◽  
Interval Censored Data ◽  
Interval Censored

scholarly journals Model evaluation and variable selection for interval-censored data

2015 ◽  
Author(s):  
◽  
Tyler Cook
Keyword(s):  
Survival Analysis ◽  
Variable Selection ◽  
Survival Time ◽  
Censored Data ◽  
Survival Data ◽  
Failure Time ◽  
Dependent Censoring ◽  
Interval Censored Data ◽  
Interval Censored ◽  
Selection For

Survival analysis is a popular area of statistics dealing with time-to-event data. A special characteristic of survival data is the presence of censoring. Censoring occurs when the survival time is only partially known. In medical studies, censoring can be caused by patients dropping out of the study before their disease event occurs. This dissertation focuses on the analysis of interval-censored data, where the failure time is only known to belong to some interval of observation times. One problem researchers face when analyzing survival data is how to handle the censoring distribution. This is an important consideration because sometimes a patient's survival time is related to the time they drop out of the study. It is often assumed that these two times are unrelated, so special methods need to be developed when they are dependent. Part of this dissertation investigates the effectiveness of methods developed for interval-censored data with dependent censoring when the censoring is actually independent. The results of these simulation studies can provide guidelines for deciding between models when facing a practical problem where one is unsure about the dependence of the censoring distribution. Another important problem seen in survival analysis is variable selection. For example, doctors might want to identify a set of diagnostic tests or measurements that can predict patient survival. We propose an imputation approach for variable selection of interval-censored data that utilizes penalized likelihood procedures. This work is significant because researchers currently do not have many tools to select important variables related to the survival time for interval-censored data.

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.

scholarly journals A multiple imputation approach for semiparametric cure model with interval censored data

2016 ◽  
Vol 99 ◽  
pp. 105-114 ◽  
Author(s):  
Jie Zhou ◽  
Jiajia Zhang ◽  
Alexander C. McLain ◽  
Bo Cai
Keyword(s):  
Multiple Imputation ◽  
Censored Data ◽  
Interval Censored Data ◽  
Cure Model ◽  
Interval Censored ◽  
Imputation Approach

scholarly journals A Semiparametric Regression Cure Model for Interval-Censored Data

2009 ◽  
Vol 104 (487) ◽  
pp. 1168-1178 ◽  
Author(s):  
Hao Liu ◽  
Yu Shen
Keyword(s):  
Censored Data ◽  
Semiparametric Regression ◽  
Interval Censored Data ◽  
Cure Model ◽  
Interval Censored
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