[ABE-L] Minicurso PIPGEs: Efficient Joint Modeling of Longitudinal and Survival Data through the User-Friendly INLAjoint R Package (12 e 13 de agosto na UFSCar)

[Rafael Izbicki]

Caros,

Em agosto teremos um minicurso no PIPGEs UFSCar/ICMC-USP com Denis Rustand
and Elias T. Krainski (KAUST).
Seguem as informações.

Efficient Joint Modeling of Longitudinal and Survival Data through the
User-Friendly INLAjoint R Package

📅 12 e 13 de agosto
⏰ 9-12hs; 14-17hs
📍 Sala 42, Departamento de Estatística, UFSCar

Ministrantes:
- Denis Rustand and Elias T. Krainski (KAUST)

Resumo:
Regression models for longitudinal and survival data have become
increasingly important in medical research. Integrated Nested Laplace
Approximation (INLA) is a fast and flexible algorithm that has been shown
to outperform current state-of-the-art methods in terms of computational
time, making it an ideal tool for personalized medicine. This short course
covers the latest developments in survival analysis and multivariate joint
longitudinal-survival models, emphasizing the benefits of using INLA for
efficient model fitting and dynamic risk predictions. The R package
INLAjoint simplifies the process of using INLA for joint models by
providing a user-friendly interface and a flexible modeling framework. It
can handle a wide range of models, allowing researchers to analyze complex
medical data.

Público-Alvo:
This short course is designed for students and researchers who are
interested in learning about the INLA methodology in the context of fitting
models for longitudinal and survival data. Prior knowledge of mixed effects
model theory and survival analysis basics is recommended, but advanced
statistical knowledge is not required. The course is suitable for
participants who are new to joint modeling and have limited experience with
statistical modeling. The course is designed to be accessible and
user-friendly, including illustrative examples on using the INLA and
INLAjoint R packages. Overall, the course is of intermediate difficulty
level.

Mais informações em anexo.

          Rafael

--
Rafael Izbicki
Assistant Professor | Vice Director of Graduate Studies
Department of Statistics
Federal University of São Carlos (UFSCar)
www.rafaelizbicki.com
www.small.ufscar.br
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