Joint models with multiple longitudinal outcomes and a time-to-event outcome: a corrected two-stage approach
From MaRDI portal
Publication:2195844
DOI10.1007/S11222-020-09927-9zbMath1447.62117arXiv1808.07719OpenAlexW3102264011MaRDI QIDQ2195844
Dimitris Rizopoulos, Isabella Kardys, Eric Boersma, Katya Mauff, Ewout W. Steyerberg
Publication date: 27 August 2020
Published in: Statistics and Computing (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1808.07719
Directional data; spatial statistics (62H11) Applications of statistics to biology and medical sciences; meta analysis (62P10) Reliability and life testing (62N05)
Related Items (8)
A numerically stable algorithm for integrating Bayesian models using Markov melding ⋮ Causal mediation analysis between resistance exercise and reduced risk of cardiovascular disease based on the Aerobics Center Longitudinal Study ⋮ Interoperability of statistical models in pandemic preparedness: principles and reality ⋮ Spatial joint models through Bayesian structured piecewise additive joint modelling for longitudinal and time-to-event data ⋮ Bayesian inference and dynamic prediction for multivariate longitudinal and survival data ⋮ A two-stage approach for Bayesian joint models: reducing complexity while maintaining accuracy ⋮ Diagnostics for a two-stage joint survival model ⋮ Pairwise estimation of multivariate longitudinal outcomes in a Bayesian setting with extensions to the joint model
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Robust specification of the roughness penalty prior distribution in spatially adaptive Bayesian P-splines models
- Generating random correlation matrices based on vines and extended onion method
- Assessing the association between trends in a biomarker and risk of event with an application in pediatric HIV/AIDS
- Joint Models for Longitudinal and Time-to-Event Data
- Dynamic Predictions and Prospective Accuracy in Joint Models for Longitudinal and Time-to-Event Data
- Improved dynamic predictions from joint models of longitudinal and survival data with time-varying effects using P-splines
- Development and validation of a dynamic prognostic tool for prostate cancer recurrence using repeated measures of posttreatment PSA: a joint modeling approach
- A Joint Model for Longitudinal Measurements and Survival Data in the Presence of Multiple Failure Types
- Semiparametric Modeling of Longitudinal Measurements and Time‐to‐Event Data–A Two‐Stage Regression Calibration Approach
- A Joint Model for Survival and Longitudinal Data Measured with Error
- Dynamic predictions with time‐dependent covariates in survival analysis using joint modeling and landmarking
- Joint Models for Multivariate Longitudinal and Multivariate Survival Data
- A Flexible B‐Spline Model for Multiple Longitudinal Biomarkers and Survival
This page was built for publication: Joint models with multiple longitudinal outcomes and a time-to-event outcome: a corrected two-stage approach