Classification rules for identifying individuals at high risk of developing myocardial infarction based on ApoB, ApoA1 and the ratio were determined using a Bayesian approach

DOI10.1080/02664763.2016.1270912OpenAlexW2562234255MaRDI QIDQ5138979

No author found.

Publication date: 4 December 2020

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

Full work available at URL: https://doi.org/10.1080/02664763.2016.1270912

zbMATH Keywords

AUCconditional logistic regressionBayesian approachMIApoA1ApoB

Mathematics Subject Classification ID

Statistics (62-XX)

Uses Software

• R
• WinBUGS
• boa
• R2WinBUGS
• aplore3

Cites Work

• Unnamed Item
• Inference from iterative simulation using multiple sequences
• Simulation Run Length Control in the Presence of an Initial Transient
• Bayesian Modeling Using WinBUGS
• Bayesian Measures of Model Complexity and Fit
• Applied Logistic Regression
• A new look at the statistical model identification