A Bayesian approach for analyzing case 2 interval-censored data under the semiparametric proportional odds model

2011 ◽  
Vol 81 (7) ◽  
pp. 876-883 ◽  
Author(s):  
Lianming Wang ◽  
Xiaoyan Lin
Keyword(s):  
Censored Data ◽  
Bayesian Approach ◽  
Proportional Odds Model ◽  
Interval Censored Data ◽  
Proportional Odds ◽  
Interval Censored

Testing the proportional odds model for interval-censored data

2006 ◽  
Vol 13 (1) ◽  
pp. 37-50 ◽  
Author(s):  
Jianguo Sun ◽  
Liuquan Sun ◽  
Chao Zhu
Keyword(s):  
Censored Data ◽  
Proportional Odds Model ◽  
Interval Censored Data ◽  
Proportional Odds ◽  
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.

scholarly journals Cure rate proportional odds models with spatial frailties for interval-censored data

2017 ◽  
Vol 24 (6) ◽  
pp. 605-625 ◽  
Author(s):  
Bao Yiqi ◽  
Vicente Garibay Cancho ◽  
Francisco Louzada ◽  
Adriano Kamimura Suzuki
Keyword(s):  
Censored Data ◽  
Interval Censored Data ◽  
Cure Rate ◽  
Proportional Odds ◽  
Interval Censored ◽  
Proportional Odds Models

scholarly journals Bayesian Estimations of Exponential Distribution Based on Interval-Censored Data with a Cure Fraction

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Al Omari Mohammed Ahmed
Keyword(s):  
Exponential Distribution ◽  
Censored Data ◽  
Bayesian Approach ◽  
Failure Time ◽  
Upper And Lower Bounds ◽  
Interval Censored Data ◽  
Squared Error ◽  
Cure Fraction ◽  
Interval Censored ◽  
The Bayesian Approach

Censored data are considered to be of the interval type where the upper and lower bounds of an event’s failure time cannot be directly observed but only determined between interval inspection times. The analyses of interval-censored data have attracted attention because they are common in the fields of reliability and medicine. A proportion of patients enrolled in clinical trials can sometimes be cured. In some instances, their symptoms mostly disappear without any recurrence of the disease. In this study, the proportion of such patients who are cured is estimated. Furthermore, the Bayesian approach under the gamma prior and maximum likelihood estimation (MLE) is used to estimate the cure fraction depending on the bounded cumulative hazard (BCH) model based on interval-censored data with an exponential distribution. The Bayesian approach uses three loss functions: squared error, linear exponential, and general entropy. These functions are compared with the MLE and used between estimators. Moreover, they are obtained using the mean squared error, which locates the best option to estimate the parameter of an exponential distribution. The results show that the BCH model and lambda parameter of the exponential distribution based on the interval-censored data can be best estimated using the Bayesian gamma prior with a positive loss function of the linear exponential.

Interval-censored data with misclassification: a Bayesian approach

2020 ◽  
pp. 1-17
Author(s):  
Magda Carvalho Pires ◽  
Enrico Antônio Colosimo ◽  
Guilherme Augusto Veloso ◽  
Raquel de Souza Borges Ferreira
Keyword(s):  
Censored Data ◽  
Bayesian Approach ◽  
Interval Censored Data ◽  
Interval Censored
Sign in / Sign up

Export Citation Format

Share Document