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14: Multilevel Joint Model of Longitudinal Continuous and Binary Outcomes for Hierarchically Structured

Seongho Song Co-Author
University of Cincinnati
 
Rhonda Szczesniak Co-Author
Cincinnati's Children's Hospital Medical Center
 
Grace Chen Zhou First Author
St. Jude Children's Research Hospital
 
Grace Chen Zhou Presenting Author
St. Jude Children's Research Hospital
 
Wednesday, Aug 6: 10:30 AM - 12:20 PM
0871 
Contributed Posters 
Music City Center 
The joint model offers a strategy to simultaneously incorporate latent associations between different types of outcomes. Recent Bayesian approaches have enhanced the flexibility and application of these models. However, there is a lack of Bayesian joint models for multilevel hierarchical data that include both longitudinal and binary outcomes. We aim to propose such a prognostic joint model for timely intervention before pulmonary exacerbation during CF progression. Additionally, we will demonstrate the biases that can arise if center effects are ignored in multicenter data.

Reference: Zhou GC, Song S, Szczesniak RD. Multilevel joint model of longitudinal continuous and binary outcomes for hierarchically structured data. Statistics in Medicine. 2023; 42(17): 2914–2927. doi: 10.1002/sim.9758

Keywords

multicenter registry data

Bayesian joint model

Hamiltonian Monte Carlo

symmetric power link family 

Main Sponsor

International Chinese Statistical Association