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

DOI10.1016/j.spl.2011.02.034zbMath1219.62054OpenAlexW2000823219MaRDI QIDQ544660

Lianming Wang, Xiaoyan Lin

Publication date: 15 June 2011

Published in: Statistics \& Probability Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.spl.2011.02.034

zbMATH Keywords

logistic distributiondata augmentationmonotone splines

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Numerical computation using splines (65D07) Applications of statistics to biology and medical sciences; meta analysis (62P10) Censored data models (62N01) Bayesian inference (62F15)

Related Items (9)

Regression analysis of bivariate current status data under the gamma-frailty proportional hazards model using the EM algorithm ⋮ Bayesian Inferences for Panel Count Data and Interval-Censored Data with Nonparametric Modeling of the Baseline Functions ⋮ Variable selection in the high-dimensional continuous generalized linear model with current status data ⋮ A flexible, computationally efficient method for fitting the proportional hazards model to interval-censored data ⋮ A flexible Bayesian non-parametric approach for fitting the odds to case II interval-censored data ⋮ A Unified Framework for Fitting Bayesian Semiparametric Models to Arbitrarily Censored Survival Data, Including Spatially Referenced Data ⋮ A Bayesian proportional hazards model for general interval-censored data ⋮ Semiparametric Bayesian accelerated failure time model with interval-censored data ⋮ Nonparametric test for stratum effects in the cox model with interval-censored data

Cites Work

• Bayesian analysis of generalized odds-rate hazards models for survival data
• Grid methods in simulation and random variate generation
• The statistical analysis of interval-censored failure time data.
• Semiparametric proportional odds models for spatially correlated survival data
• Regression analysis of interval-censored failure time data with linear transformation models
• Mixtures of g Priors for Bayesian Variable Selection
• Proportional odds regression and sieve maximum likelihood estimation
• Sieve Estimation for the Proportional-Odds Failure-Time Regression Model With Interval Censoring
• Using Conditional Logistic Regression to Fit Proportional Odds Models to Interval Censored Data
• Bayesian Analysis of Binary and Polychotomous Response Data
• Bayesian Semiparametric Proportional Odds Models
• Bayesian auxiliary variable models for binary and multinomial regression

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