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Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
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Multilevel joint model of longitudinal continuous and binary outcomes for hierarchically structured data.

Grace Chen Zhou1,2, Seongho Song2, Rhonda D Szczesniak1,3

  • 1Division of Biostatistics & Epidemiology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA.

Statistics in Medicine
|May 11, 2023
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Summary

This study introduces a multilevel joint model for cystic fibrosis (CF) research, improving analysis of lung function and exacerbations in multicenter trials. Accounting for center effects enhances model accuracy and prediction for clinical studies.

Keywords:
Bayesian joint modelcystic fibrosisflexible link functionmulticenternon-hierarchical bias

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Area of Science:

  • Biostatistics
  • Longitudinal Data Analysis
  • Clinical Research Methodology

Background:

  • Joint models commonly link longitudinal and survival data but infrequently address hierarchical structures or binary outcomes.
  • Multilevel modeling is crucial for multicenter studies but less explored within joint modeling frameworks.
  • Existing joint models often neglect nested effects, limiting their application in complex clinical data like that from cystic fibrosis registries.

Purpose of the Study:

  • To propose a novel multilevel joint model integrating linear mixed-effects (LME) and generalized linear mixed-effects (GLMM) submodels using a Bayesian approach.
  • To apply this model to analyze lung function and pulmonary exacerbations in cystic fibrosis (CF) patients across multiple centers.
  • To evaluate the bias introduced by non-hierarchical models and the benefits of incorporating center-specific effects.

Main Methods:

  • Developed a Bayesian multilevel joint model combining LME for continuous longitudinal outcomes and GLMM for binary outcomes.
  • Incorporated nested effects (center effects) within the LME submodel to account for hierarchical data structures.
  • Utilized a symmetric power exponential power (spep) link function in the GLMM to model center-specific parameters.

Main Results:

  • The multilevel joint model significantly improved estimation and prediction accuracy by including center effects and individual variation in the LME submodel.
  • Simulation studies confirmed the bias reduction and improved performance of the hierarchical model compared to non-hierarchical approaches.
  • Application to US CF Foundation Patient Registry data demonstrated the importance of accounting for center-specific variations in lung function.

Conclusions:

  • Multilevel joint models are essential for accurately analyzing hierarchical longitudinal data, particularly in multicenter clinical studies.
  • Incorporating center-specific effects and appropriate link functions (like spep) enhances the reliability of findings in populations like cystic fibrosis.
  • This approach offers improved clinical insights for managing diseases with complex, multi-level data structures.