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Joint Bayesian longitudinal models for mixed outcome types and associated model selection techniques.

Nicholas Seedorff1, Grant Brown1, Breanna Scorza2

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This study introduces a new Bayesian longitudinal model to predict leishmaniosis progression in dogs using both ordinal and continuous data. The model shows improved prediction accuracy, aiding clinical decision-making with multiple disease measures.

Keywords:
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Area of Science:

  • Veterinary epidemiology
  • Biostatistics
  • Infectious disease modeling

Background:

  • Leishmaniosis is a significant canine disease requiring accurate progression monitoring.
  • Existing methods may not fully leverage combined ordinal and continuous health data.
  • Longitudinal data analysis is crucial for understanding disease dynamics.

Purpose of the Study:

  • To develop and validate a novel Bayesian longitudinal model for jointly analyzing mixed-type outcomes in canine leishmaniosis.
  • To assess the predictive performance of the proposed multivariate model compared to traditional approaches.
  • To identify a suitable model selection criterion for this complex data structure.

Main Methods:

  • Development of a Bayesian longitudinal model incorporating autoregressive errors.
  • Joint analysis of ordinal (e.g., clinical scores) and continuous (e.g., biomarker levels) leishmaniosis progression data.
  • Simulation studies to evaluate model performance and prediction accuracy.

Main Results:

  • The proposed Bayesian model demonstrated superior prediction accuracy over traditional Bayesian hierarchical models in simulations.
  • The multivariate approach effectively borrowed strength across different data types for enhanced forecasting.
  • An appropriate model selection criterion was identified for practical application.

Conclusions:

  • The developed Bayesian longitudinal model offers a promising tool for clinical settings, especially with multiple disease indicators.
  • This approach enhances the ability to forecast disease progression by integrating diverse data types.
  • It supports improved clinical decision-making in managing canine leishmaniosis.