Integrating sample similarities into latent class analysis: a tree-structured shrinkage approach

Authors

Mengbing Li, Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA.
Daniel E. Park, Environmental and Occupational Health, Milken Institute School of Public Health, The George Washington University, Washington, District of Columbia, USA.
Maliha Aziz, Environmental and Occupational Health, Milken Institute School of Public Health, The George Washington University, Washington, District of Columbia, USA.
Cindy M. Liu, Environmental and Occupational Health, Milken Institute School of Public Health, The George Washington University, Washington, District of Columbia, USA.
Lance B. Price, Environmental and Occupational Health, Milken Institute School of Public Health, The George Washington University, Washington, District of Columbia, USA.
Zhenke Wu, Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA.

Document Type

Journal Article

Publication Date

3-1-2023

Journal

Biometrics

Volume

79

Issue

1

DOI

10.1111/biom.13580

Keywords

Gaussian diffusion; latent class models; phylogenetic tree; spike-and-slab prior; variational Bayes; zoonotic infectious diseases

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.

APA Citation

Li, Mengbing; Park, Daniel E.; Aziz, Maliha; Liu, Cindy M.; Price, Lance B.; and Wu, Zhenke, "Integrating sample similarities into latent class analysis: a tree-structured shrinkage approach" (2023). GW Authored Works. Paper 2661.
https://hsrc.himmelfarb.gwu.edu/gwhpubs/2661

Department

Environmental and Occupational Health