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A Bayesian Multilevel Joint Modeling of Longitudinal and Survival Outcomes in Cluster Randomized Controlled Trial
Yixiu Liu1, Depeng Jiang1, Mahmoud Torabi1
1College of Community and Global Health, University of Manitoba, Winnipeg, Manitoba, Canada.
Statistics in Medicine
|January 22, 2026
Summary
A new multilevel joint model (MJM) accurately analyzes clustered data from cluster randomized controlled trials (CRCTs). This method improves mental health intervention analysis by accounting for hierarchical structures, preventing biased results.
Area of Science:
- Biostatistics
- Public Health
- Developmental Psychology
Background:
- Cluster randomized controlled trials (CRCTs) are essential for group-level interventions but require methods accounting for inherent multilevel data structures.
- Traditional joint models (JMs) for longitudinal and survival data do not adequately handle the clustering common in CRCTs.
- Ignoring hierarchical data structures in CRCTs can lead to biased inferences and underestimated intervention effects.
Purpose of the Study:
- To introduce a novel multilevel joint model (MJM) designed to analyze correlated longitudinal and survival outcomes within CRCT data.
- To evaluate the effectiveness of the PAX Good Behavior Game, a classroom-based mental health intervention, using the proposed MJM.
- To assess the performance of the MJM through simulation studies under various data conditions.
Main Methods:
- Development and application of a multilevel joint model (MJM) to analyze data from a large CRCT.
- The MJM simultaneously models mental health trajectories (longitudinal) and time-to-mental disorder diagnosis (survival).
- Simulation studies were conducted to evaluate MJM performance across different censoring rates, cluster sizes, and variance components.
Main Results:
- The PAX program demonstrated significant improvements in mental health trajectories and reduced the risk of mental disorder diagnoses.
- The MJM provided more accurate parameter estimates and standard errors compared to traditional JMs.
- Simulation results confirmed that ignoring clustering in CRCT data leads to biased estimates and underestimation of intervention effects.
Conclusions:
- The proposed MJM is a robust and flexible framework for analyzing complex clustered data from CRCTs.
- Accounting for hierarchical structures is crucial for accurate evaluation of group-based interventions.
- The MJM enhances the reliability of findings in public health and educational intervention research.
Keywords:
Bayesiancluster randomized controlled trailgroup‐based intervention/treatmentmultilevel joint model of longitudinal and survival dataMore Related Videos
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