Generalized Transformation Cure Rate Models for Survival Data Analysis

• Activity Number: 619
• Type: Contributed
• Date/Time: Thursday, August 4, 2011 : 8:30 AM to 10:20 AM
• Sponsor: Biometrics Section
• Author(s): Yang Liu *+ and Xu-Feng Niu
• Companies: Merck & Co., Inc. and Florida State University
• Keywords: survival analysis ; cure rate model ; fractional polynomials

Abstract

In typical survival data analysis, it is often assumed that all uncensored subjects will eventually experience the event of interest. However, sometimes this assumption is not true and survival models with a cure fraction are more appropriate. Considering the non-linear relationship between response variable and covariates, we propose a generalized transformation cure rate model with fractional polynomials motivated by Zeng et al's (2006) transformed proportional time cure model. In this talk, we will show (i) statistical properties of the proposed model: including identifiability, asymptotic consistency, and asymptotic normality of the estimated regression coefficients, and (ii) a power selection procedure to fit fractional polynomials within the proposed model. A simulation study is carried out to show the performance of the proposed power selection procedure. The generalized transformation cure rate models are applied to coronary heart disease and cancer related medical data from both observational cohort studies and clinical trials.