A novel Bayesian continuous piecewise linear log-hazard model, with estimation and inference via reversible jump Markov chain Monte Carlo

DOI10.1002/sim.8511zbMATH Open1546.62141MaRDI QIDQ6627380

Taylor Peak, Ashok Hemal, Andrew G. Chapple

Publication date: 29 October 2024

Published in: Statistics in Medicine (Search for Journal in Brave)

zbMATH Keywords

survival analysisreversible jump Markov chain Monte CarloBayesian methodshazard estimationCox models

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10)

Cites Work

• Unnamed Item
• Reversible jump Markov chain Monte Carlo computation and Bayesian model determination
• Flexible Bayesian survival modeling with semiparametric time-dependent and shape-restricted covariate effects
• Piecewise exponential models for survival data with covariates
• Bayesian variable selection for a semi-competing risks model with three hazard functions
• Bayesian adaptive B-spline estimation in proportional hazards frailty models
• Piecewise linear approximations for cure rate models and associated inferential issues
• Bayesian Semiparametric Models for Survival Data with a Cure Fraction
• The separation of timescales in Bayesian survival modeling of the time-varying effect of a time-dependent exposure
• The Analysis of Rates and of Survivorship Using Log-Linear Models
• Life Tables with Concomitant Information
• Bayesian Measures of Model Complexity and Fit
• A hybrid phase I‐II/III clinical trial design allowing dose re‐optimization in phase III
• A practical guide to splines.
• Bayesian semiparametric analysis of semicompeting risks data: investigating hospital readmission after a pancreatic cancer diagnosis

Related Items (1)

An augmented illness-death model for semi-competing risks with clinically immediate terminal events

This page was built for publication: A novel Bayesian continuous piecewise linear log-hazard model, with estimation and inference via reversible jump Markov chain Monte Carlo