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Integrating sample similarities into latent class analysis: a tree‐structured shrinkage approach
Author(s) -
Li Mengbing,
Park Daniel E.,
Aziz Maliha,
Liu Cindy M.,
Price Lance B.,
Wu Zhenke
Publication year - 2023
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/biom.13580
Subject(s) - inference , tree (set theory) , latent class model , bayes' theorem , computer science , similarity (geometry) , multivariate statistics , mathematics , class (philosophy) , latent variable , bayesian inference , data mining , artificial intelligence , statistics , bayesian probability , mathematical analysis , image (mathematics)
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.