A Bayesian inference for the penalized spline joint models of longitudinal and time-to-event data: a prior sensitivity analysis
From MaRDI portal
Publication:1985373
DOI10.1515/mcma-2020-2058zbMath1436.62470OpenAlexW3008836595MaRDI QIDQ1985373
Publication date: 7 April 2020
Published in: Monte Carlo Methods and Applications (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1515/mcma-2020-2058
Directional data; spatial statistics (62H11) Numerical computation using splines (65D07) Applications of statistics to biology and medical sciences; meta analysis (62P10) Bayesian inference (62F15) Estimation in survival analysis and censored data (62N02)
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Weak convergence and optimal scaling of random walk Metropolis algorithms
- Inference from iterative simulation using multiple sequences
- Bayesian and frequentist regression methods
- Joint Models for Longitudinal and Time-to-Event Data
- Joint modelling of longitudinal and time-to-event data with application to predicting abdominal aortic aneurysm growth and rupture
- Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
- Partial likelihood
- Jointly Modeling Longitudinal and Event Time Data With Application to Acquired Immunodeficiency Syndrome
- Penalized spline joint models for longitudinal and time-to-event data
- Equation of State Calculations by Fast Computing Machines
- Monte Carlo sampling methods using Markov chains and their applications
- A Flexible B‐Spline Model for Multiple Longitudinal Biomarkers and Survival
- Linear mixed models for longitudinal data
- Statistical models based on counting processes
This page was built for publication: A Bayesian inference for the penalized spline joint models of longitudinal and time-to-event data: a prior sensitivity analysis