Seminário UFSCar/USP - Sequential Monte Carlo methods in Bayesian joint models for longitudinal and time-to-event data - Palestrante: Danilo Alvares da Silva (Universidad Católica de Chile)

Seminário do Programa Interinstitucional de Pós-Graduação em Estatística - PIPGEs
UFSCar/USP

Data e Horário:
21/08/2020 às 14h

Local:
Google Meet - Link da apresentação (até uma hora antes da palestra em www.pipges.ufscar.br.)

Título:
Sequential Monte Carlo methods in Bayesian joint models for longitudinal and time-to-event data

Palestrante:
Danilo Alvares da Silva (U.C. de Chile)

Resumo:
The statistical analysis of the information generated by medical follow-up is a very important challenge in the field of personalized medicine. As the evolutionary course of a patient's disease progresses, his/her medical follow-up generates more and more information that should be processed immediately in order to review and update his/her prognosis and treatment. Hence, we focus on this update process through sequential inference methods for joint models of longitudinal and time-to-event data from a Bayesian perspective. More specifically, we propose the use of sequential Monte Carlo (SMC) methods for static parameter joint models with the intention of reducing computational time in each update of the full Bayesian inferential process. Our proposal is very general and can be easily applied to the most popular joint models approaches. We illustrate the use of the presented sequential methodology in a joint model with competing risk events for a real scenario involving patients on mechanical ventilation in intensive care units (ICUs).