A Bayesian Latent Class Model to Predict Kidney Obstruction in the Absence of Gold Standard
DOI10.1080/01621459.2019.1689983zbMath1452.62797OpenAlexW2988771337WikidataQ126865067 ScholiaQ126865067MaRDI QIDQ5146016
Qi Long, Jeong Hoon Jang, Andrew T. Taylor, Amita K. Manatunga, Changgee Chang
Publication date: 22 January 2021
Published in: Journal of the American Statistical Association (Search for Journal in Brave)
Full work available at URL: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8188835
factor analysislatent class modelBayesian predictionjoint modelinghierarchical probit modelkidney obstruction
Factor analysis and principal components; correspondence analysis (62H25) Applications of statistics to biology and medical sciences; meta analysis (62P10) Bayesian inference (62F15)
Cites Work
- Sparse Bayesian infinite factor models
- 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
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- Finite Mixture Modeling with Mixture Outcomes Using the EM Algorithm
- Bayesian Latent Factor Regression for Functional and Longitudinal Data
- Bayesian Analysis of Binary and Polychotomous Response Data
- Random Effects Modeling Approaches for Estimating ROC Curves from Repeated Ordinal Tests without a Gold Standard
- A Probit Latent Class Model with General Correlation Structures for Evaluating Accuracy of Diagnostic Tests
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