【学术报告】第35期

摘要

There is a global trend that the average onset age of many human complex diseases is decreasing and the age of cancer patients becomes more spread out. The age effect on survival is nonlinear in practice and identification of the pattern is important for optimal prognostic decision. This paper considers estimation of the potentially nonlinear age effect for general partly linear survival models to ensure a valid statistical inference on the treatment effect. A simple and efficient sieve maximum likelihood estimation method that can be implemented easily using standard statistical softwares is proposed.  A data-driven adaptive algorithm to determine the optimal locations and the number of knots is suggested to identify some possible change points where the age effect is very different before and after these points.  Simulation studies are carried out to study the performance of the proposed method.  For illustration purpose, the method is applied to a breast cancer data set from the public domain to study the disease free survival of the patients. The results revealed that the risk is highest among young patients and young postmenopausal patients, probably due to a change in hormonal environment during a certain phase of menopause.

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