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A Bayesian joint model for mediation analysis with matrix-valued mediators.

Zijin Liu1, Zhihui Amy Liu1,2, Ali Hosni2

  • 1Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario M5T 3M7, Canada.

Biometrics
|December 13, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a new Bayesian model to analyze how radiation therapy (RT) dose affects treatment interruptions, using dose-volume histograms (DVH) from organs at risk (OARs). The method improves understanding of mediation effects for better cancer treatment planning.

Keywords:
Bayesian methodsdose-volume histogramshigh-dimensional mediation analysismatrix-valued mediatorsradiotherapy treatment planning

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Area of Science:

  • Biostatistics
  • Radiation Oncology
  • Medical Physics

Background:

  • Unscheduled treatment interruptions in radiation therapy (RT) can compromise patient care quality.
  • Understanding the relationship between RT prescription dose, organs at risk (OARs) radiation exposure, and treatment interruptions is crucial for optimizing treatment planning.
  • Dose-volume histograms (DVH) provide a matrix-valued representation of OARs radiation exposure, posing challenges for traditional mediation analysis.

Purpose of the Study:

  • To propose a novel Bayesian joint mediation model capable of handling high-dimensional matrix-valued mediators, specifically DVH data.
  • To investigate the mediation effects of OARs radiation exposure on the relationship between RT prescription dose and treatment interruptions.
  • To develop methods for extracting latent features from matrix-valued data and identifying significant mediation pathways.

Main Methods:

  • Development of a Bayesian joint mediation model incorporating an adaptation of probabilistic multilinear principal components analysis (MPCA) for matrix-valued DVH data.
  • Implementation of a Gibbs sampling algorithm for joint estimation of model parameters.
  • Application of Varimax rotation to identify active mediation indicators within the matrix-valued data.
  • Simulation studies to compare the proposed model's efficiency against a two-step method.

Main Results:

  • The proposed Bayesian joint model demonstrates higher efficiency in estimating causal decomposition effects compared to a two-step approach.
  • The model successfully identifies and visualizes mediation effects within the matrix structure of DVH data.
  • The method was applied to analyze the impact of prescription dose on treatment interruptions in anal canal cancer patients.

Conclusions:

  • The novel Bayesian joint mediation model provides an effective framework for analyzing high-dimensional matrix-valued mediators like DVH.
  • This approach enhances the understanding of how radiation dose distribution in OARs influences treatment interruptions.
  • The findings can inform future radiation therapy planning to minimize interruptions and improve patient outcomes.