Seminário UFSCar/USP - Long-term frailty modeling using a non-proportional hazards model: Application with a melanoma dataset - Palestrante: Eder Angelo Milani

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

Data e Horário:
05/06/2020 às 14h

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

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 do 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