A Bayesian hierarchical model for prediction of latent health states from multiple data sources with application to active surveillance of prostate cancer

DOI10.1111/biom.12577zbMath1372.62063arXiv1508.07511OpenAlexW2964114032WikidataQ31122903 ScholiaQ31122903MaRDI QIDQ5283323

Scott L. Zeger, Kenneth J. Pienta, Mufaddal Mamawala, Rebecca Yates Coley, H. Ballentine Carter, Aaron J. Fisher

Publication date: 21 July 2017

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1508.07511

zbMATH Keywords

missing datarisk classificationlatent class analysisprecision medicineprostate cancer prognosis

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Medical applications (general) (92C50)

Related Items (4)

A numerically stable algorithm for integrating Bayesian models using Markov melding ⋮ prediction-prostate-surveillance ⋮ Optimizing active surveillance for prostate cancer using partially observable Markov decision processes ⋮ Unnamed Item

Uses Software

• R
• JAGS
• R2jags
• GitHub
• PRMLT

Cites Work

• Unnamed Item
• Unnamed Item
• Random-Effects Models for Longitudinal Data
• Missing Data in Longitudinal Studies
• Modeling Disease Progression With Longitudinal Markers
• The Calculation of Posterior Distributions by Data Augmentation
• Latent Class Models for Joint Analysis of Longitudinal Biomarker and Event Process Data
• Modelling Progression of CD4-Lymphocyte Count and Its Relationship to Survival Time
• Efficient parametrisations for normal linear mixed models
• Domain-Level Covariance Analysis for Multilevel Survey Data With Structured Nonresponse
• Joint modelling of longitudinal measurements and event time data

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