Composite likelihood and maximum likelihood methods for joint latent class modeling of disease prevalence and high-dimensional semicontinuous biomarker data

Bo Zhang, Wei Liu*, Hui Zhang, Qihui Chen, Zhiwei Zhang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Joint latent class modeling of disease prevalence and high-dimensional semicontinuous biomarker data has been proposed to study the relationship between diseases and their related biomarkers. However, statistical inference of the joint latent class modeling approach has proved very challenging due to its computational complexity in seeking maximum likelihood estimates. In this article, we propose a series of composite likelihoods for maximum composite likelihood estimation, as well as an enhanced Monte Carlo expectation–maximization (MCEM) algorithm for maximum likelihood estimation, in the context of joint latent class models. Theoretically, the maximum composite likelihood estimates are consistent and asymptotically normal. Numerically, we have shown that, as compared to the MCEM algorithm that maximizes the full likelihood, not only the composite likelihood approach that is coupled with the quasi-Newton method can substantially reduce the computational complexity and duration, but it can simultaneously retain comparative estimation efficiency.

Original languageEnglish (US)
Pages (from-to)425-449
Number of pages25
JournalComputational Statistics
Volume31
Issue number2
DOIs
StatePublished - Jun 1 2016

Keywords

  • Expectation–maximization algorithm
  • Markov chain Monte Carlo
  • Pseudo-likelihood
  • Shared latent class models
  • Two-part models

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty
  • Computational Mathematics

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