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Joint longitudinal and time-to-event models for multilevel hierarchical data.
Samuel L Brilleman1,2, Michael J Crowther3, Margarita Moreno-Betancur2,4,5
1Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia.
Statistical Methods in Medical Research
|November 1, 2018
Summary
This study introduces a novel joint model for hierarchical longitudinal and time-to-event data, specifically analyzing tumor burden and progression-free survival in non-small cell lung cancer patients using a Bayesian framework.
Area of Science:
- Biostatistics
- Medical Statistics
- Survival Analysis
Background:
- Joint models for longitudinal and time-to-event data are increasingly complex.
- Handling hierarchical data structures is crucial in applied research.
Purpose of the Study:
- To propose a joint model for hierarchical longitudinal and time-to-event data.
- To analyze the association between tumor burden and progression-free survival in non-small cell lung cancer (NSCLC).
Main Methods:
- Developed a three-level hierarchical joint model (measurements within lesions within patients).
- Modeled lesion-specific longitudinal trajectories and patient-specific risk of progression.
- Specified novel association structures linking lower-level clusters to patient-level summaries.
- Utilized a Bayesian framework with user-friendly software.
Main Results:
- The proposed model effectively handles complex three-level hierarchical data.
- Demonstrated application in modeling tumor burden and progression-free survival in NSCLC.
- The novel association structures integrate information across lesions for patient-level insights.
Conclusions:
- The developed joint model provides a robust framework for hierarchical longitudinal and time-to-event data.
- Offers valuable insights into the relationship between tumor burden and survival outcomes in NSCLC.
- Discusses implications for models with higher-level clustering factors.
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