[ABE-L] Convite: Seminário DEST/UFMG em 19/11/2021.

[Vinicius Mayrink]

Caros,

Na próxima sexta-feira (*19 de novembro*, às *13:30h*) o ciclo de
Seminários do *Departamento de Estatística da UFMG* terá a
apresentação de *Vera
L. D. Tomazella*.

Vera é professora do Departamento de Estatística da UFSCar e obteve o grau
de Doutora em Ciência da Computação e Matemática Computacional pela USP.
Ela tem experiência de pesquisa pós-doc pela Universidade de Valência
(Espanha) e pela Universidade de Manchester (Reino Unido). Atua
principalmente nas seguintes áreas: Análise de sobrevivência,
Confiabilidade, Modelos de risco, Análise de diagnósticos, Inferência
Bayesiana, Modelos Não-Lineares.

O seminário será transmitido ao vivo pelo canal do Youtube "*Seminários
DEST - UFMG <https://www.youtube.com/channel/UCoZC2_pME9ca_-Hx4djd60w>*".

Att,
Vinícius Mayrink

*********** Título e Resumo ***********

Vera Lúcia Damasceno Tomazella (Departamento de Estatística, UFSCar)

*Nonproportional hazards model with a frailty term for modeling subgroups
with evidence of long-term survivors: Application to a lung cancer dataset.*

With advancements in medical treatments for cancer, an increase in the life
expectancy of patients undergoing new treatments is expected. Consequently,
the field of statistics has evolved to present increasingly flexible models
to explain such results better. In this paper, we present a lung cancer
dataset with some covariates that exhibit nonproportional hazards (NPHs).
Besides, the presence of long‐term survivors is observed in subgroups. The
proposed modeling is based on the generalized time‐dependent logistic model
with each subgroup's effect time and a random term effect (frailty). In
practice, essential covariates are not observed for several reasons. In
this context, frailty models are useful in modeling to quantify the amount
of unobservable heterogeneity. The frailty distribution adopted was the
weighted Lindley distribution, which has several interesting properties,
such as the Laplace transform function on closed form, flexibility in the
probability density function, among others. The proposed model allows for
NPHs and long‐term survivors in subgroups. We exemplify this model's use by
applying data of patients diagnosed with lung cancer in the state of São
Paulo, Brazil.

-- 
*Vinícius D. Mayrink*
*Professor Associado - Departamento de Estatística*

*ICEx, Universidade Federal de Minas Gerais*
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