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Proportional hazards (PH), proportional odds (PO) and accelerated failuretime (AFT) models have been widely used to deal with survival data in differentfields of knowledge. Despite their popularity, such models are not suitable tohandle survival data with crossing survival curves. Yang and Prentice (2005)proposed a semiparametric two-sample approach, denoted here as the YP model,allowing the analysis of crossing survival curves and including the PH and POconfigurations as particular cases. In a general regression setting, thepresent work proposes a fully likelihood-based approach to fit the YP model.The main idea is to model the baseline hazard via the piecewise exponential(PE) distribution. The approach shares the flexibility of the semiparametricmodels and the tractability of the parametric representations. An extensivesimulation study is developed to evaluate the performance of the proposedmodel. In addition, we demonstrate how useful is the new method through theanalysis of survival times related to patients enrolled in a cancer clinicaltrial. The simulation results indicate that our model performs well formoderate sample sizes in the general regression setting. A superior performanceis also observed with respect to the original YP model designed for thetwo-sample scenario.

A fully likelihood-based approach to model survival data with crossing survival curves