9 papers
The R package LSJM for fitting location-scale joint models for a longitudinal marker and complex survival data
Léonie Courcoul, Hélène Jacqmin-Gadda, Antoine Barbieri
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 inc…
Built-in Selection Bias in Proportional Hazards Models with Omitted Covariates: Simulation Evidence and Alternative Approaches
Ayoub Bifenzi, Helene Jacqmin-Gadda
In time-to-event analysis, the hazard ratio (HR) derived from the Cox proportional hazards (PH) model is the most commonly used and widely reported measure for assessing treatment…
Asymmetric Laplace distribution regression model for fitting heterogeneous longitudinal response
Antoine Barbieri, Angelo Alcaraz, Mouna Abed +2
The systematic collection of longitudinal data is very common in practice, making mixed models widely used. Most developments around these models focus on modeling the mean traject…
A regularized multi-state model for covariate selection with interval-censored survival data
Ariane Bercu, Agathe Guilloux, Cécile Proust-Lima +1
In population-based cohorts, disease diagnoses are typically censored by intervals as made during scheduled follow-up visits. The exact disease onset time is thus unknown, and in t…
Dynamic prediction of an event using multiple longitudinal markers: a model averaging approach
Reza Hashemi, Taban Baghfalaki, Viviane Philipps +1
Dynamic event prediction, using joint modeling of survival time and longitudinal variables, is extremely useful in personalized medicine. However, the estimation of joint models in…
A Two-stage Joint Modeling Approach for Multiple Longitudinal Markers and Time-to-event Data
Taban Baghfalaki, Reza Hashemi, Catherine Helmer +1
Collecting multiple longitudinal measurements and time-to-event outcomes is a common practice in clinical and epidemiological studies, often focusing on exploring associations betw…
A Two-Stage Bayesian Approach for Variable Selection in Joint Modeling of Multiple Longitudinal Markers with Competing Risks
Taban Baghfalaki, Reza Hashemi, Christophe Tzourio +2
In many clinical and epidemiological studies, collecting longitudinal measurements together with time-to-event outcomes is essential. Accurately estimating the association between…
Joint model for interval-censored semi-competing events and longitudinal data with subject-specific within and between visits variabilities
Léonie Courcoul, Catherine Helmer, Antoine Barbieri +1
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 facto…
Joint Modeling of Longitudinal Measurements and Time-to-event Outcomes Using BUGS
Taban Baghfalaki, Mojtaba Ganjali, Antoine Barbieri +2
The objective of this paper is to provide an introduction to the principles of Bayesian joint modeling of longitudinal measurements and time-to-event outcomes, as well as model imp…