[ABE-L] Seminário PIPGEs - 05/06/2020 às 14h00 - Eder A. Milani

[Sandro Gallo]

Prezados (as), muito bom dia!

Espero que estejam bem.

Segue o link da palestra do PIPGEs desta tarde com descrição abaixo:
meet.google.com/jbc-qvvc-src

*Palestra*

*Data e Horário: *05/06/2020 às 14h

*Local: *Google Meet - Link da apresentação:  meet.google.com/jbc-qvvc-src

*Título:  *Long-term frailty modeling using a non-proportional hazards
model: Application with a melanoma dataset

*Palestrante: *Eder Angelo Milani

*Resumo:*

The semiparametric Cox regression model is often fitted in the modeling of
survival data. One of its main advantages is the ease of interpretation, as
long as the hazards rates for two individuals do not vary over time.
However, the proportionality assumption of the hazards may not be true in
some situations. In addition, in several survival data is common a
proportion of units  not susceptible to the event of interest, even if,
accompanied by a sufficiently large time, which is so-called immune,
“cured,” or not susceptible to the event of interest. In this context,
several cure rate models are available to deal with the long-term. Here, we
consider the generalized time-dependent logistic (GTDL) model with a power
variance function (PVF) frailty term introduced in the hazard function to
control for unobservable heterogeneity in patient populations. It allows
for non-proportional hazards, as well as survival data with long-term
survivors. Parameter estimation was performed using the maximum likelihood
method, and Monte Carlo simulation was conducted to evaluate the
performance of the models. Its practice relevance is illustrated in a real
medical dataset from a population-based study of incident cases of melanoma
diagnosed in the state of São Paulo, Brazil.

*Autores: *Eder A. Milani, Vinicius F. Calsavara, Eduardo Bertolli and Vera
Tomazella


Lembramos, também, que todos os seminários e outras atividades já previstas
encontram-se na página inicial do PIPGES: http://www.pipges.ufscar.br/.

Abraço, Sandro
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