When Do Latent Class Models Overstate Accuracy for Diagnostic and Other Classifiers in the Absence of a Gold Standard?

DOI10.1111/J.1541-0420.2011.01694.XzbMath1274.92028OpenAlexW2056320586WikidataQ37849985 ScholiaQ37849985MaRDI QIDQ2912354

Bruce D. Spencer

Publication date: 14 September 2012

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/j.1541-0420.2011.01694.x

zbMATH Keywords

sensitivityvalidityspecificitylatent class modeldiagnostic testclassification errorjury

Mathematics Subject Classification ID

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

Related Items (1)

Clustering construction on a multimodal probability model

Cites Work

• Unnamed Item
• Conditional association and unidimensionality in monotone latent variable models
• Nonparametric estimation of component distributions in a multivariate mixture
• Latent Class Modeling Approaches for Assessing Diagnostic Error without a Gold Standard: With Applications to p53 Immunohistochemical Assays in Bladder Tumors
• A Cautionary Note on the Robustness of Latent Class Models for Estimating Diagnostic Error without a Gold Standard
• Insights into latent class analysis of diagnostic test performance
• Estimating the Error Rates of Diagnostic Tests
• A Biomedical Application of Latent Class Models with Random Effects
• Random Effects Models in Latent Class Analysis for Evaluating Accuracy of Diagnostic Tests
• Latent Variable Modeling of Diagnostic Accuracy
• Using Latent Class Models to Characterize and Assess Relative Error in Discrete Measurements
• "Cost-Utility" as a Measure of the Efficiency of a Test
• A Probit Latent Class Model with General Correlation Structures for Evaluating Accuracy of Diagnostic Tests

This page was built for publication: When Do Latent Class Models Overstate Accuracy for Diagnostic and Other Classifiers in the Absence of a Gold Standard?