The proportional odds model for multivariate interval-censored failure time data

2007 ◽  
Vol 26 (28) ◽  
pp. 5147-5161 ◽  
Author(s):  
Man-Hua Chen ◽  
Xingwei Tong ◽  
Jianguo Sun
Keyword(s):  
Failure Time ◽  
Proportional Odds Model ◽  
Failure Time Data ◽  
Proportional Odds ◽  
Time Data ◽  
Interval Censored

scholarly journals Semiparametric Odds Rate Model for Modeling Short-Term and Long-Term Effects with Application to a Breast Cancer Genetic Study

2014 ◽  
Vol 10 (2) ◽  
Author(s):  
Mengdie Yuan ◽  
Guoqing Diao
Keyword(s):  
Breast Cancer ◽  
Genetic Study ◽  
Failure Time ◽  
Proportional Odds Model ◽  
Failure Time Data ◽  
Rate Model ◽  
Proportional Odds ◽  
Time Data ◽  
Short Term

AbstractThe proportional odds model is commonly used in the analysis of failure time data. The assumption of constant odds ratios over time in the proportional odds model, however, can be violated in some applications. Motivated by a genetic study with breast cancer patients, we propose a novel semiparametric odds rate model for the analysis of right-censored survival data. The proposed model incorporates the short-term and long-term covariate effects on the failure time data and includes the proportional odds model as a nested model. We develop efficient likelihood-based inference procedures and establish the large sample properties of the proposed nonparametric maximum likelihood estimators. Simulation studies demonstrate that the proposed methods perform well in practical settings. An application to the motivating example is provided.

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.

Regression analysis of interval-censored failure time data with possibly crossing hazards

2017 ◽  
Vol 37 (5) ◽  
pp. 768-775 ◽  
Author(s):  
Han Zhang ◽  
Peijie Wang ◽  
Jianguo Sun
Keyword(s):  
Regression Analysis ◽  
Failure Time ◽  
Failure Time Data ◽  
Time Data ◽  
Interval Censored ◽  
Crossing Hazards
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