scholarly journals Partially linear transformation cure models for interval-censored data

2016 ◽  
Vol 93 ◽  
pp. 257-269 ◽  
Author(s):  
Tao Hu ◽  
Liming Xiang
Keyword(s):  
Censored Data ◽  
Linear Transformation ◽  
Interval Censored Data ◽  
Partially Linear ◽  
Cure Models ◽  
Interval Censored

Semi-Parametric Cure Rate Proportional Odds Models with Spatial Frailties for Interval-Censored Data

2019 ◽  
Vol 11 (03n04) ◽  
pp. 1950005
Author(s):  
Yiqi Bao ◽  
Vicente G. Cancho ◽  
Francisco Louzada ◽  
Adriano K. Suzuki
Keyword(s):  
Censored Data ◽  
Real Data ◽  
Interval Censored Data ◽  
Spatial Correlations ◽  
Cure Rate ◽  
Proportional Odds ◽  
Data Set ◽  
Cure Rate Models ◽  
Cure Models ◽  
Interval Censored

In this work, we proposed the semi-parametric cure rate models with independent and dependent spatial frailties. These models extend the proportional odds cure models and allow for spatial correlations by including spatial frailty for the interval censored data setting. Moreover, since these cure models are obtained by considering the occurrence of an event of interest is caused by the presence of any nonobserved risks, we also study the complementary cure model, that is, the cure models are obtained by assuming the occurrence of an event of interest is caused when all of the nonobserved risks are activated. The MCMC method is used in a Bayesian approach for inferential purposes. We conduct an influence diagnostic through the diagnostic measures in order to detect possible influential or extreme observations that can cause distortions on the results of the analysis. Finally, the proposed models are applied to the analysis of a real data set.

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.

The Cox-Aalen model for left-truncated and mixed interval-censored data

Statistics ◽  
2019 ◽  
Vol 53 (5) ◽  
pp. 1152-1167 ◽  
Author(s):  
Pao-sheng Shen ◽  
Li Ning Weng
Keyword(s):  
Censored Data ◽  
Interval Censored Data ◽  
Aalen Model ◽  
Interval Censored
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