A local likelihood proportional hazards model for interval censored data

2001 ◽  
Vol 21 (2) ◽  
pp. 263-275 ◽  
Author(s):  
Rebecca A. Betensky ◽  
Jane C. Lindsey ◽  
Louise M. Ryan ◽  
M. P. Wand
Keyword(s):  
Censored Data ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Local Likelihood ◽  
Interval Censored Data ◽  
Hazards Model ◽  
Interval Censored

A Bayesian proportional hazards model for general interval-censored data

2014 ◽  
Vol 21 (3) ◽  
pp. 470-490 ◽  
Author(s):  
Xiaoyan Lin ◽  
Bo Cai ◽  
Lianming Wang ◽  
Zhigang Zhang
Keyword(s):  
Censored Data ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Interval Censored Data ◽  
Hazards Model ◽  
Interval Censored

scholarly journals An extended proportional hazards model for interval-censored data subject to instantaneous failures

2019 ◽  
Vol 26 (1) ◽  
pp. 158-182
Author(s):  
Prabhashi W. Withana Gamage ◽  
Monica Chaudari ◽  
Christopher S. McMahan ◽  
Edwin H. Kim ◽  
Michael R. Kosorok
Keyword(s):  
Censored Data ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Interval Censored Data ◽  
Hazards Model ◽  
Interval Censored ◽  
Instantaneous Failures ◽  
Data Subject

scholarly journals A Bayesian approach for analyzing partly interval-censored data under the proportional hazards model

2020 ◽  
Vol 29 (11) ◽  
pp. 3192-3204
Author(s):  
Chun Pan ◽  
Bo Cai ◽  
Lianming Wang
Keyword(s):  
Censored Data ◽  
Survival Data ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Research Effort ◽  
Cox Proportional Hazards Model ◽  
Interval Censored Data ◽  
Cancer Trial ◽  
Hazards Model ◽  
Interval Censored

Partly interval-censored time-to-event data often occur in biomedical studies of diseases where periodic medical examinations for symptoms of interest are necessary. Recent decades have seen blooming methods and R packages for interval-censored data; however, the research effort for partly interval-censored data is limited. We propose an efficient and easy-to-implement Bayesian semiparametric method for analyzing partly interval-censored data under the proportional hazards model. Two simulation studies are conducted to compare the performance of the proposed method with two main Bayesian methods currently available in the literature and the classic Cox proportional hazards model. The proposed method is applied to a partly interval-censored progression-free survival data from a metastatic colorectal cancer trial.

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