Describing complex disease progression using joint latent class models for multivariate longitudinal markers and clinical endpoints

DOI10.1002/SIM.9844zbMATH Open1548.62399MaRDI QIDQ6626929

Tiphaine Saulnier, Wassilios G. Meissner, Anne Pavy-Le Traon, Patrice Péran, Viviane Philipps, Cécile Proust-Lima, Alexandra Foubert-Samier, Olivier Rascol

Publication date: 29 October 2024

Published in: Statistics in Medicine (Search for Journal in Brave)

zbMATH Keywords

clusteringheterogeneityjoint modelingmultivariate longitudinal dataneurodegenerative diseasesclass-membershipmultiple system atrophy

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10)

Cites Work

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• Score Test for Conditional Independence Between Longitudinal Outcome and Time to Event Given the Classes in the Joint Latent Class Model
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• Regularized Latent Class Model for Joint Analysis of High‐Dimensional Longitudinal Biomarkers and a Time‐to‐Event Outcome
• Joint latent class model for longitudinal data and interval‐censored semi‐competing events: Application to dementia
• Joint modelling of longitudinal measurements and event time data

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