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

Palestrante: Vera Lucia Damasceno Tomazella (UFSCar)
Data: 23/06/2021
Horário: 15:30h
Local: Transmissão online

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. Our approach enables us to accommodate unobservable heterogeneity and non-proportional hazards, as well as survival data with long-term survivors. 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.

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