A joint-modeling approach to assess the impact of biomarker variability on the risk of developing clinical outcome

DOI10.1007/S10260-010-0150-ZzbMath1333.62273OpenAlexW2156364310WikidataQ34577844 ScholiaQ34577844MaRDI QIDQ261568

The Ocular Hypertension Study (OHTS) Group, Julia A. Beiser, J. Philip Miller, Mae Gordon, Chengjie Xiong, Feng Gao

Publication date: 24 March 2016

Published in: Statistical Methods and Applications (Search for Journal in Brave)

Full work available at URL: http://europepmc.org/articles/pmc3039885

zbMATH Keywords

longitudinal datasurvival dataMarkov chain Monte Carlo (MCMC)joint modelpatient-specific varianceWinBUGS

Mathematics Subject Classification ID

Linear regression; mixed models (62J05) Applications of statistics to biology and medical sciences; meta analysis (62P10)

Related Items (2)

A flexible link for joint modelling longitudinal and survival data accounting for individual longitudinal heterogeneity ⋮ A Bayesian Joint Dispersion Model with Flexible Links

Uses Software

• WinBUGS
• R2WinBUGS
• rms
• BayesDA

Cites Work

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• Bayesian Measures of Model Complexity and Fit
• Regression Dilution in the Proportional Hazards Model
• Modeling the Drop-Out Mechanism in Repeated-Measures Studies
• Joint modelling of longitudinal measurements and event time data
• Regression modeling strategies. With applications to linear models, logistic regression and survival analysis

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