arXiv subjects

Léonie Courcoul

Publications and source records attributed to Léonie Courcoul.

3 recordsLinked to original sources

arXiv2026

The R package LSJM for fitting location-scale joint models for a longitudinal marker and complex survival data

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.

stat.CO↗

arXiv2024

Joint model for interval-censored semi-competing events and longitudinal data with subject-specific within and between visits variabilities

Dementia currently affects about 50 million people worldwide, and this number is rising. Since there is still no cure, the primary focus remains on preventing modifiable risk factors such as cardiovascular factors. It is now recognized that high blood pressure is a risk factor for dementia. An increasing number of studies suggest that blood pressure variability may also be a risk factor for dementia. However, these studies have significant methodological weaknesses and fail to distinguish between long-term and short-term variability. The aim of this work was to propose a new joint model that combines a mixed-effects model, which handles the residual variance distinguishing inter-visit variability from intra-visit variability, and an illness-death model that allows for interval censoring and semi-competing risks. A subject-specific random effect is included in the model for both variances. Risks can simultaneously depend on the current value and slope of the marker, as well as on each of the two components of the residual variance. The model estimation is performed by maximizing the likelihood function using the Marquardt-Levenberg algorithm. A simulation study validates the estimation procedure, which is implemented in an R package. The model was estimated using data from the Three-City (3C) cohort to study the impact of intra- and inter-visits blood pressure variability on the risk of dementia and death.

stat.ME↗

arXiv2024

A location-scale joint model for studying the link between the time-dependent subject-specific variability of blood pressure and competing events

Given the high incidence of cardio and cerebrovascular diseases (CVD), and its association with morbidity and mortality, its prevention is a major public health issue. A high level of blood pressure is a well-known risk factor for these events and an increasing number of studies suggest that blood pressure variability may also be an independent risk factor. However, these studies suffer from significant methodological weaknesses. In this work we propose a new location-scale joint model for the repeated measures of a marker and competing events. This joint model combines a mixed model including a subject-specific and time-dependent residual variance modeled through random effects, and cause-specific proportional intensity models for the competing events. The risk of events may depend simultaneously on the current value of the variance, as well as, the current value and the current slope of the marker trajectory. The model is estimated by maximizing the likelihood function using the Marquardt-Levenberg algorithm. The estimation procedure is implemented in a R-package and is validated through a simulation study. This model is applied to study the association between blood pressure variability and the risk of CVD and death from other causes. Using data from a large clinical trial on the secondary prevention of stroke, we find that the current individual variability of blood pressure is associated with the risk of CVD and death. Moreover, the comparison with a model without heterogeneous variance shows the importance of taking into account this variability in the goodness-of-fit and for dynamic predictions.

stat.ME↗