A Bayesian approach to restricted latent class models for scientifically-structured clustering of multivariate binary outcomes

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Abstract

This paper presents a model-based method for clustering multivariate binary observations that incorporates constraints consistent with the scientific context. The approach is motivated by the precision medicine problem of identifying autoimmune disease patient subsets who may require different treatments. We start with a family of restricted latent class models or RLCMs (e.g., Xu and Shang, 2018). However, in the motivating example and many others like it, the unknown number of subsets and the definitions of the latent classes are among the targets of inference. We use a Bayesian approach to RCLMs in order to use informative prior assumptions on the number and definitions of latent classes to be consistent with scientific knowledge so that the posterior distribution tends to concentrate on smaller numbers of clusters and sparser binary patterns. The paper presents a novel posterior inference algorithm to handle discrete mixture parameters. Through simulations under the assumed model and realistic deviations from it, we demonstrate greater interpretability of results and superior finite-sample clustering performance for our method compared to common alternatives. The methods are illustrated with an analysis of protein data to detect clusters representing autoantibody classes among scleroderma patients.

Original languageEnglish (US)
JournalUnknown Journal
DOIs
StatePublished - Aug 25 2018

Keywords

  • Autoimmune disease
  • Clustering
  • Dependent Binary Data
  • Latent Class Models
  • Markov Chain Monte Carlo
  • Measurement Error
  • Mixture of Finite Mixture Models
  • Scleroderma

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)
  • Immunology and Microbiology(all)
  • Neuroscience(all)
  • Pharmacology, Toxicology and Pharmaceutics(all)

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