Screening for prostate cancer using multivariate mixed-effects models

DOI10.1080/02664763.2011.644523OpenAlexW2065217263WikidataQ36009869 ScholiaQ36009869MaRDI QIDQ5127029

Shan Sheng, Larry J. Brant, Christopher H. Morrell, E. Jeffrey Metter

Publication date: 21 October 2020

Published in: Journal of Applied Statistics (Search for Journal in Brave)

Full work available at URL: http://europepmc.org/articles/pmc3367770

zbMATH Keywords

sensitivityclassificationlongitudinal dataspecificitydisease screening

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (3)

A joint normal‐binary (probit) model ⋮ Frequentist and Bayesian approaches for a joint model for prostate cancer risk and longitudinal prostate-specific antigen data ⋮ A comparison of group prediction approaches in longitudinal discriminant analysis

Cites Work

• Unnamed Item
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• Analysis of multivariate longitudinal data using quasi-least squares
• A Linear Mixed-Effects Model With Heterogeneity in the Random-Effects Population
• Predicting renal graft failure using multivariate longitudinal profiles
• Modeling Disease Progression With Longitudinal Markers
• A Random-Effects Model for Multiple Characteristics With Possibly Missing Data
• Analysis of Multivariate Longitudinal Outcomes With Nonignorable Dropouts and Missing Covariates
• Latent Class Models for Joint Analysis of Longitudinal Biomarker and Event Process Data
• Latent Variable Models for Longitudinal Data with Multiple Continuous Outcomes
• Multiple Imputation and Posterior Simulation for Multivariate Missing Data in Longitudinal Studies
• Pairwise Fitting of Mixed Models for the Joint Modeling of Multivariate Longitudinal Profiles
• A parametric empirical Bayes method for cancer screening using longitudinal observations of a biomarker

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