Joint modelling of longitudinal and repeated time-to-event data using nonlinear mixed-effects models and the stochastic approximation expectation–maximization algorithm

DOI10.1080/00949655.2013.878938zbMath1457.62355OpenAlexW2773890221MaRDI QIDQ5220804

Kevin Bleakley, Cyprien Mbogning, Marc Lavielle

Publication date: 27 March 2020

Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00949655.2013.878938

zbMATH Keywords

maximum likelihoodSAEM algorithmmixed-effects modelsjoint modelsrepeated time-to-events

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Applications of statistics to biology and medical sciences; meta analysis (62P10) Point estimation (62F10) Estimation in survival analysis and censored data (62N02)

Related Items (1)

f-SAEM: a fast stochastic approximation of the EM algorithm for nonlinear mixed effects models

Uses Software

• R
• Monolix
• SemiPar
• invGauss

Cites Work

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• Linear mixed models for longitudinal data

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