A New Parametric Yang-Prentice Regression Model With Applications to Real-Life Survival Medical Data With Crossing Survival Curves

Abstract

The Yang and Prentice (YP) regression models have attracted considerable attention in the scientific community due to their ability to handle survival data with crossing hazard functions. These models encompass both the proportional hazards (PH) and proportional odds (PO) models as special cases. A key feature of the YP framework is the inclusion of distinct short-term and long-term hazard ratio parameters, which allow it to accommodate intersecting survival curves. Notably, the original YP model leaves the baseline hazard function unspecified. In this study, a fully parametric method is introduced for fitting the YP model within a general regression context. The core idea involves modeling the baseline hazard using the exponentiated-Weibull distribution, which provides both the flexibility of parametric modeling and analytical tractability. To assess the effectiveness of the proposed approach, comprehensive simulation studies were performed. The results indicate that the model performs robustly even with moderate sample sizes and demonstrates improved accuracy compared to the original YP model, particularly in general regression scenarios beyond the traditional two-sample setup. Additionally, the utility and effectiveness of the proposed method are illustrated through applications to real-world datasets. The results underscore the model’s strengths in capturing complex survival patterns and enhancing the analysis of survival data.