Long-term survival, health-related quality of life, and quality-adjusted life year expectancy of cancer patients admitted to the ICU are limited. Nevertheless, these clinical outcomes exhibit wide variability among patients and are associated with simple characteristics present at the time of ICU admission, which may help healthcare professionals estimate patients' prognoses.

Copula models have become increasingly popular for modeling multivariate survival data. In this paper we review some of the recent work that has been appeared for copula model for bivariate survival data and propose a Bayesian modeling. Our approach is very flexible with respect to the choice of marginal distributions and, depending on the copula model employed, it is possible to have a class of variation for the dependence parameter. We compare some of the copula models using a descriptive diagnostic method and three popular Bayesian model selection criteria. Our methodology is illustrated with the Diabetic Retinopathy Study (1976).

Censoring is a common feature in survival data, usually associated with loss to follow-up. However, when the fraction of censored data is high, it may indicate that part of the experimental units are no longer at risk of presenting the event of interest. In this article we consider the approach of Chen et al. (1999) for such situation, and discuss the case where covariates may be measured with error. Simulations and an application to a real dataset are also presented.

In this paper, an algorithm is developed to compute estimates for parameters in destructive weighted Poisson cure rate models. It is shown, analytically, the robustness of the procedure with respect to the maximization of the observed likelihood function. The approach allows a simple implementation of different distributions for non-destroyed concurrent causes. The performance of the method is evaluated through simulation studies and by analysing a real data set related to patients with malignant melanoma.

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