The R package LSJM for fitting location-scale joint models for a longitudinal marker and complex survival data
Abstract
The LSJM R package provides a series of functions to estimate models based on location-scale linear mixed model and location-scale joint model theory. For longitudinal data, it includes the estimation of location-scale mixed models with subject-specific residual variance that can be time-dependent or distinguish within-visit and between-visits variability. When survival data is considered, implemented joint models handle competing risks as well as semi-competing risks possibly with interval-censored data through the use of a illness-death sub-model. They incorporate flexible dependence structure between the longitudinal marker and the times-to-events through the current value, the slope and/or the residual variability of the marker. Maximum likelihood estimators are obtained using the Marquardt-Levenberg algorithm with stringent convergence criteria. The package also provides several post-fit functions including goodness-of-fit analyses and individual dynamic predictions. Some examples on real data allows to illustrates the use of this package.
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BibTeX
@misc{courcoul2026package,
title = {{The R package LSJM for fitting location-scale joint models for a longitudinal marker and complex survival data}},
author = {Léonie Courcoul and Hélène Jacqmin-Gadda and Antoine Barbieri},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.03990},
eprint = {2610.03990},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.03990},
}
RIS
TY - UNPB
TI - The R package LSJM for fitting location-scale joint models for a longitudinal marker and complex survival data
AU - Léonie Courcoul
AU - Hélène Jacqmin-Gadda
AU - Antoine Barbieri
PY - 2026
DA - 2026/10/02
PB - arXiv
DO - 10.48550/arxiv.2610.03990
UR - https://arxiv.org/abs/2610.03990
AB - The LSJM R package provides a series of functions to estimate models based on location-scale linear mixed model and location-scale joint model theory. For longitudinal data, it includes the estimation of location-scale mixed models with subject-specific residual variance that can be time-dependent or distinguish within-visit and between-visits variability. When survival data is considered, implemented joint models handle competing risks as well as semi-competing risks possibly with interval-censored data through the use of a illness-death sub-model. They incorporate flexible dependence structure between the longitudinal marker and the times-to-events through the current value, the slope and/or the residual variability of the marker. Maximum likelihood estimators are obtained using the Marquardt-Levenberg algorithm with stringent convergence criteria. The package also provides several post-fit functions including goodness-of-fit analyses and individual dynamic predictions. Some examples on real data allows to illustrates the use of this package.
ER -