PT  - JOURNAL ARTICLE
AU  - Mengbing Li
AU  - Daniel E. Park
AU  - Maliha Aziz
AU  - Cindy M. Liu
AU  - Lance B. Price
AU  - Zhenke Wu
TI  - Integrating Sample Similarities into Latent Class Analysis: A Tree-Structured Shrinkage Approach
AID  - 10.1101/2021.01.25.428033
DP  - 2021 Jan 01
TA  - bioRxiv
PG  - 2021.01.25.428033
4099  - http://biorxiv.org/content/early/2021/08/02/2021.01.25.428033.short
4100  - http://biorxiv.org/content/early/2021/08/02/2021.01.25.428033.full
AB  - 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 (LCMs) 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.Competing Interest StatementThe authors have declared no competing interest.