Yakovlev Promotion Time Cure Model with Local Polynomial Estimation

• Activity Number: 616 - New Advances in Semiparametric Modeling and Testing for Complex Data
• Type: Topic Contributed
• Date/Time: Thursday, August 2, 2018 : 8:30 AM to 10:20 AM
• Sponsor: Section on Nonparametric Statistics
• Author(s): Li-Hsiang Lin* and Li-Shan Huang
• Companies: Georgia Tech and Institute of Statistics, National Tsing Hua University, TAIWAN
• Keywords: Censored data; local likelihood; proportional hazards model; survival analysis

Abstract

In modeling survival data with a cure fraction, flexible modeling of covariate effects on the probability of cure has important medical implications, which aids investigators in identifying better treatments to cure. This paper studies a semiparametric form of the Yakovlev promotion time cure model that allows for nonlinear effects of a continuous covariate. We adopt the local polynomial approach and use the local likelihood criterion to derive nonlinear estimates of covariate effects on cure rates, assuming that the baseline distribution function follows a parametric form. This way we adopt a flexible method to estimate the cure rate, the important part in cure models, and a convenient way to estimate the baseline function, which is less useful in practice. An algorithm is proposed to implement estimation at both the local and global scale. Asymptotic properties of local polynomial estimates, the nonparametric part, are investigated, and the parametric part is shown to be root-n consistent. The proposed methods are illustrated by simulations and real data analysis.