[ABE-L] Seminários do Programa de Pós Graduação em Estatística/UFRGS – A Poisson–Birnbaum–Saunders Mixture Cure Model: An Application to Breast Cancer Data

[Márcia Barbian]

Olá,

Temos o prazer de convidar a todas e todos para mais um seminário do
Programa de Pós-Graduação em Estatística da UFRGS.

Nosso próximo encontro ocorrerá de forma *REMOTA* no *dia 28 de julho* e
terá como palestrante a pesquisadora Márcia Brandão de Oliveira Martins -
Professora Departamento de Estatística - Universidade Federal do Amazonas
(UFAM).
*Título da palestra:*  A Poisson–Birnbaum–Saunders Mixture Cure Model: An
Application to Breast Cancer Data

*Resumo: *We introduce a new modelling for long-term survival models,
assuming that the number of competing causes follows a mixture of Poisson
and the Birnbaum-Saunders distribution. In this context, we present some
statistical properties of our model and demonstrate that the promotion time
model emerges as a limiting case. We delve into detailed discussions of
specific models within this class. Notably, we examine the expected number
of competing causes, which depends on covariates. This allows for direct
modeling of the cure rate as a function of covariates.We present an
Expectation-Maximization (EM) algorithm for parameter estimation, to
discuss the estimation via maximum likelihood (ML) and provide insights
into parameter inference for this model. Additionally, we outline
sufficient conditions for ensuring the consistency and asymptotic normal
distribution of ML estimators. To evaluate the performance of our
estimation method, we conduct a Monte Carlo simulation to provide
asymptotic properties and a power study of LR test by contrasting our
methodology against the promotion time model. To demonstrate the
practical applicability of our model, we apply it to a real medical dataset
from a population-based study of incidence of breast cancer in São Paulo,
Brazil. Our results illustrate that the proposed model can outperform
traditional approaches in terms of model fitting, highlighting its
potential utility in real-world scenarios.

*Data: 28/07/2026*

*Horário: 13h30*
*Local: *https://mconf.ufrgs.br/webconf/ppgest

Abraço,

Márcia Barbian

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