• A Bayesian Multilevel Joint Mo...

Treffer: A Bayesian Multilevel Joint Modeling of Longitudinal and Survival Outcomes in Cluster Randomized Controlled Trial Studies.

Title:
A Bayesian Multilevel Joint Modeling of Longitudinal and Survival Outcomes in Cluster Randomized Controlled Trial Studies.
Authors:
Liu Y; College of Community and Global Health, University of Manitoba, Winnipeg, Manitoba, Canada., Jiang D; College of Community and Global Health, University of Manitoba, Winnipeg, Manitoba, Canada., Torabi M; College of Community and Global Health, University of Manitoba, Winnipeg, Manitoba, Canada., Zhang X; Department of Mathematics and Statistics, University of Victoria, Victoria, British Columbia, Canada.
Source:
Statistics in medicine [Stat Med] 2026 Jan; Vol. 45 (1-2), pp. e70385.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Wiley Country of Publication: England NLM ID: 8215016 Publication Model: Print Cited Medium: Internet ISSN: 1097-0258 (Electronic) Linking ISSN: 02776715 NLM ISO Abbreviation: Stat Med Subsets: MEDLINE
Imprint Name(s):
Original Publication: Chichester ; New York : Wiley, c1982-
MeSH Terms:
Randomized Controlled Trials as Topic*/statistics & numerical data , Randomized Controlled Trials as Topic*/methods , Multilevel Analysis* , Models, Statistical*, Humans ; Bayes Theorem ; Longitudinal Studies ; Survival Analysis ; Cluster Analysis ; Computer Simulation ; Female ; Male ; Child ; Mental Disorders/therapy ; Mental Disorders/diagnosis
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Grant Information:
RGPIN-2022-04124 Natural Sciences and Engineering Research Council of Canada; 202003PJT Canadian Institute of Health Research; University of Manitoba,
Contributed Indexing:
Keywords: Bayesian; cluster randomized controlled trail; group‐based intervention/treatment; multilevel joint model of longitudinal and survival data
Entry Date(s):
Date Created: 20260122 Date Completed: 20260122 Latest Revision: 20260124
Update Code:
20260124
PubMed Central ID:
PMC12824832
DOI:
10.1002/sim.70385
PMID:
41567118
Database:
MEDLINE

Weitere Informationen

Cluster randomized controlled trials (CRCTs) are commonly used when interventions are delivered at the group level. Since data from CTCTs are inherently multilevel, methods that properly account for clustering are required. Joint modeling (JM) of longitudinal and survival data allows for simultaneous evaluation of intervention effects on repeated measures and time-to-event outcomes, offering a comprehensive view of intervention effects. However, existing JMs do not accommodate clustered data structures typically of CRCTs. This study introduces a multilevel joint model (MJM) to simultaneously evaluate intervention effects on correlated longitudinal and survival outcomes. The model was applied to empirical data from a large CRCT evaluating the PAX Good Behavior Game, a classroom-based mental health intervention involving 4189 Grade 1 students across 313 classrooms during the 2011-2012 school year. Mental health was assessed at three time points: pre-PAX (January 2012), post-PAX (June 2012), and Grade 5 (June 2016). Time-to-first mental disorder diagnosis was tracked through March 2024. Simulation studies further evaluated the MJM's performance under varying conditions, including censoring rates, cluster sizes, group-level variances, and survival model specifications. Results indicated the PAX program significantly improved mental health trajectories and reduced the risk of mental disorder diagnoses. The MJM outperformed traditional JMs by producing more accurate estimates and standard errors. Both empirical and simulation findings demonstrated that ignoring hierarchical structures leads to biased inferences and underestimation of intervention effects. The proposed MJM offers a robust and flexible analytic framework for analyzing data from CRCTs, emphasizing the importance of accounting for clustering in evaluating group-based interventions.
(© 2026 The Author(s). Statistics in Medicine published by John Wiley & Sons Ltd.)