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FastJM: Semi-Parametric Joint Modeling of Longitudinal and Survival DataFastJM: Semi-Parametric Joint Modeling of Longitudinal and Survival Data

Description: Maximum likelihood estimation for the semi-parametric joint modeling of competing risks and (multivariate) longitudinal data applying customized linear scan algorithms, proposed by Li and colleagues (2022). The time-to-event data is modelled using a (cause-specific) Cox proportional hazards regression model with time-fixed covariates. The longitudinal outcome is modelled using a linear mixed effects model. The association is captured by shared random effects. The model is estimated using an Expectation Maximization algorithm.

Reference: Efficient Algorithms and Implementation of a Semiparametric Joint Model for Longitudinal and Competing Risk Data: With Applications to Massive Biobank Data Shanpeng Li, Ning Li, Hong Wang, Jin Zhou, Hua Zhou, Gang Li First published: 08 February 2022

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