Bayesian semi-parametric joint modeling of biomarker data with a latent changepoint: Assessing the temporal performance of Enzyme-Linked Immunosorbent Assay (ELISA) testing for paratuberculosis

Michelle Norris, Wesley O. Johnson, Ian Gardner

Research output: Contribution to journalArticle

2 Scopus citations

Abstract

In this paper, we develop a class of semi-parametric statistical models that can be used for the important problem of analyzing longitudinal biomarker data with the purpose of quantifying their diagnostic capabilities, as a function of time from infection. We focus on the complicated problem where there is no gold standard assessment of the actual timing of infection/disease onset (our change point), which provides additional motivation for considering a second, binary test, in order to make it easier to estimate the change points for individuals that become diseased. An important additional feature of our model is its nonparametric part, which allows for distinct biomarker responses to the insult of infection/disease. In our case, the model allows for the possibility of an unknown number of clusters of individuals, each with distinct slopes corresponding to distinct biological reactions. Clusters with steeper slopes would correspond to individuals that could be diagnosed sooner than those with more gradual slopes.

Original languageEnglish (US)
Pages (from-to)417-438
Number of pages22
JournalStatistics and its Interface
Volume7
Issue number4
DOIs
StatePublished - Jan 1 2014
Externally publishedYes

Keywords

  • Bayesian semi-parametric approach
  • Change point model
  • Dirichlet process mixture
  • Imperfect reference standard
  • Johne's disease
  • Longitudinal data
  • Random effects

ASJC Scopus subject areas

  • Statistics and Probability
  • Applied Mathematics

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