[ABE-L] Seminário DEST/UFMG em 29/10/2024

[Marcos Prates]

Caros,

Excepcionalmente, na próxima terça-feira (29 de Outubro, às 13:00h) o ciclo
de Seminários do Departamento de Estatística da UFMG terá a apresentação do
Prof. Denis Rustand da KAUST - Arábia Saudita.

Denis Rustand obtained his Ph.D. in Public Health, Biostatistics in 2020 at
University of Bordeaux, France, where he developed the joint modeling
framework for longitudinal and survival data in the context of cancer
clinical trials data analysis. He is now a Post-Doctoral fellow at KAUST
where he joined the INLA development team. He is the main developer and
maintainer of the INLAjoint R package, an user-friendly interface to fit
joint longitudinal-survival models with INLA. His research areas include
Bayesian computational statistics, survival analysis and applications of
statistics to medical research.

Título: Fast, accurate, and flexible Bayesian survival modeling with the R
package INLAjoint

Resumo: This presentation introduces INLAjoint, a user-friendly R package
that simplifies the fitting of various survival models using the
computationally efficient Integrated Nested Laplace Approximations (INLA)
method. INLA offers a significant speed advantage over traditional Markov
Chain Monte Carlo (MCMC) methods while maintaining accuracy in parameter
estimation.  INLAjoint supports a wide range of survival models, including
proportional hazards, multi-state, and joint models for multivariate
longitudinal and survival data. Joint models, which link multiple
regression submodels through correlated or shared random effects, can be
computationally intensive. In this context, we underscore the significant
reduction in computation time achieved by INLA when compared to MCMC,
without compromising on accuracy.

Beyond model fitting, the talk provides practical guidance on using the
INLAjoint R package, including detailed syntax examples. A key application
of joint models is dynamic prediction, which involves estimating the risk
of an event (e.g., death or disease progression) based on changes in
longitudinal outcomes over time. INLAjoint enables the estimation of
dynamic risk predictions and can incorporate updates to these predictions
as new longitudinal data becomes available. This makes INLAjoint a valuable
tool for analyzing complex health data.

O seminário será presencial na sala 2076 do Instituto de Ciências Exatas da
UFMG.

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Marcos Prates
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