Two-Component Mixture Cure Rate Model with Spline-Estimated Nonparametric Components

• Activity Number: 89
• Type: Contributed
• Date/Time: Sunday, July 31, 2011 : 4:00 PM to 5:50 PM
• Sponsor: Biopharmaceutical Section
• Author(s): Lu Wang*+
• Companies: Novartis Oncology
• Keywords: Survival analysis ; Nonparametric cure rate model ; Smoothing spline ; Penalized EM algorithm ; Confidence region ; Confidence region

Abstract

In some survival analysis of medical studies, there are often long term survivors who can be considered as permanently cured. The goals in these studies are to estimate the non-cured probability of the whole population and the hazard rate of the susceptible subpopulation. When covariates are present as often happens in practice, to understand covariate effects on the non-cured probability and hazard rate is of equal importance. The existing methods are limited to parametric and semiparametric models. We propose a two-component mixture cure rate model with nonparametric forms for both the cure probability and the hazard rate function. Identifiably of the model is guaranteed by an additive assumption on hazard rate. Estimation is carried out by an EM algorithm on maximizing a penalized likelihood. For inferential purpose, we apply the Louis formula to obtain point-wise confidence intervals for non-cured probability and hazard rate. Asymptotic convergence rates of our function estimates are established. We then evaluate the proposed method by extensive simulations. An application to a melanoma study reveals interesting patterns in the data.