A semiparametric accelerated failure time partial linear model and its application to breast cancer
Breast cancer is the most common non-skin cancer in women and the second most common cause of cancer-related death in US women. It is well known that the breast cancer survival rate varies with age at diagnosis. For most cancers, the relative survival rate decreases with age, but breast cancer may show an unusual age pattern. In order to reveal the stage risk and age effects pattern, we propose a semiparametric accelerated failure time partial linear model and develop its estimation method based on the penalized spline (P-spline) and the rank estimation approach. The simulation studies demonstrate that the proposed method is comparable to the parametric approach when data is not contaminated, and more stable than parametric methods when data is contaminated. By applying the proposed model and method to the breast cancer data set of Atlantic County, New Jersey, from the SEER program, we successfully reveal the significant effects of stage, and show that women diagnosed at age around 38 years have consistently higher survival rates than either younger or older women.
partial linear model penalized spline rank estimation accelerated failure time model robustness