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Covariate-guided Bayesian mixture of spline experts for the analysis of multivariate high-density longitudinal data.
Fu, Haoyi; Tang, Lu; Rosen, Ori; Hipwell, Alison E; Huppert, Theodore J; Krafty, Robert T.
Afiliação
  • Fu H; Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, United States.
  • Tang L; Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, United States.
  • Rosen O; Department of Mathematical Sciences, University of Texas at El Paso, El Paso, TX, United States.
  • Hipwell AE; Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, United States.
  • Huppert TJ; Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, United States.
  • Krafty RT; Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA, United States.
Biostatistics ; 25(3): 666-680, 2024 Jul 01.
Article em En | MEDLINE | ID: mdl-38141227
ABSTRACT
With rapid development of techniques to measure brain activity and structure, statistical methods for analyzing modern brain-imaging data play an important role in the advancement of science. Imaging data that measure brain function are usually multivariate high-density longitudinal data and are heterogeneous across both imaging sources and subjects, which lead to various statistical and computational challenges. In this article, we propose a group-based method to cluster a collection of multivariate high-density longitudinal data via a Bayesian mixture of smoothing splines. Our method assumes each multivariate high-density longitudinal trajectory is a mixture of multiple components with different mixing weights. Time-independent covariates are assumed to be associated with the mixture components and are incorporated via logistic weights of a mixture-of-experts model. We formulate this approach under a fully Bayesian framework using Gibbs sampling where the number of components is selected based on a deviance information criterion. The proposed method is compared to existing methods via simulation studies and is applied to a study on functional near-infrared spectroscopy, which aims to understand infant emotional reactivity and recovery from stress. The results reveal distinct patterns of brain activity, as well as associations between these patterns and selected covariates.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Teorema de Bayes Limite: Humans / Infant Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Teorema de Bayes Limite: Humans / Infant Idioma: En Ano de publicação: 2024 Tipo de documento: Article