  Proportional hazards (PH), proportional odds (PO) and accelerated failure
time (AFT) models have been widely used to deal with survival data in different
fields of knowledge. Despite their popularity, such models are not suitable to
handle 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 PO
configurations as particular cases. In a general regression setting, the
present 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 semiparametric
models and the tractability of the parametric representations. An extensive
simulation study is developed to evaluate the performance of the proposed
model. In addition, we demonstrate how useful is the new method through the
analysis of survival times related to patients enrolled in a cancer clinical
trial. The simulation results indicate that our model performs well for
moderate sample sizes in the general regression setting. A superior performance
is also observed with respect to the original YP model designed for the
two-sample scenario.
