Random effects models for identifying the most harmful medication errors in a large, voluntary reporting database

DOI10.1214/16-AOAS974zbMath1416.62622MaRDI QIDQ2404423

Jessica M. Franklin, Sergio Venturini, Francesca Dominici, Laura Morlock

Publication date: 18 September 2017

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

Full work available at URL: https://projecteuclid.org/euclid.aoas/1500537713

zbMATH Keywords

empirical Bayesdata miningBayesian hierarchical modelspontaneous reporting

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Empirical decision procedures; empirical Bayes procedures (62C12)

This page was built for publication: Random effects models for identifying the most harmful medication errors in a large, voluntary reporting database