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

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  • Vinicius F Calsavara
    Department of Epidemiology and Statistics, A.C.Camargo Cancer Center, São Paulo, Brazil
  • Eder A Milani
    Institute of Mathematics and Statistics, Federal University of Goiás, Goiânia, Brazil
  • Eduardo Bertolli
    Skin Cancer Department, A.C.Camargo Cancer Center, São Paulo, Brazil
  • Vera Tomazella
    Department of Statistics, Federal University of São Carlos, São Carlos, Brazil

Description

<jats:p> 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. In practice 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 in 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. </jats:p>

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