ArticleBiometrics2023
Integrating sample similarities into latent class analysis: a tree-structured shrinkage approach.
Article in Biometrics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
8 citing papers in PubMed.
- Investigating the zoonotic origins of ESBL-producingMicrobiology spectrum · 2026Article
- TREE-REGULARIZED BAYESIAN LATENT CLASS ANALYSIS FOR IMPROVING WEAKLY SEPARATED DIETARY PATTERN SUBTYPING IN SMALL-SIZED SUBPOPULATIONS.The annals of applied statistics · 2025Article
- ZoonoticmBio · 2025Article
- Accounting for uncertainty in conflict mortality estimation: an application to the Gaza War in 2023-2024.Population health metrics · 2025Article
- Tree-informed Bayesian multi-source domain adaptation: cross-population probabilistic cause-of-death assignment using verbal autopsy.Biostatistics (Oxford, England) · 2024Article
- Pediatric urinary tract infections caused by poultry-associatedMicrobiology spectrum · 2024Article
- Using source-associated mobile genetic elements to identify zoonotic extraintestinalOne health (Amsterdam, Netherlands) · 2023Article
- Bayesian estimation methods for survey data with potential applications to health disparities research.Wiley interdisciplinary reviewsArticle
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Authors and funding
6 authors.
Funding
Abstract
This paper is concerned with using multivariate binary observations to estimate the probabilities of unobserved classes with scientific meanings. We focus on the setting where additional information about sample similarities is available and represented by a rooted weighted tree. Every leaf in the given tree contains multiple samples. Shorter distances over the tree between the leaves indicate a priori higher similarity in class probability vectors. We propose a novel data integrative extension to classical latent class models with tree-structured shrinkage. The proposed approach enables (1) borrowing of information across leaves, (2) estimating data-driven leaf groups with distinct vectors of class probabilities, and (3) individual-level probabilistic class assignment given the observed multivariate binary measurements. We derive and implement a scalable posterior inference algorithm in a variational Bayes framework. Extensive simulations show more accurate estimation of class probabilities than alternatives that suboptimally use the additional sample similarity information. A zoonotic infectious disease application is used to illustrate the proposed approach. The paper concludes by a brief discussion on model limitations and extensions.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.