Induced Smoothing for the Semiparametric-Accelerated Hazards Model

• Activity Number: 64
• Type: Topic Contributed
• Date/Time: Sunday, August 4, 2013 : 4:00 PM to 5:50 PM
• Sponsor: Biometrics Section
• Author(s): Jiajia Zhang*+
• Companies: Univ of South Carolina, Arnold School of Public Health
• Keywords: Accelerated Hazards Model ; Rank Estimation ; Induced Smoothing

Abstract

Compared to the proportional hazards model and accelerated failure time model, the accelerated hazards model has a unique property in its application, in that it can allow gradual effects of the treatment. However, its application is still very limited, partly due to the complexity of existing semiparametric estimation methods. We propose a new semiparametric estimation method based on the induced smoothing and rank type estimates. The parameter estimates and their variances can be easily obtained from the smoothed estimating equation; thus it is easy to use in practice. Our numerical study shows that the new method is more efficient than the existing methods with respect to its variance estimation and coverage probability. The proposed method is employed to reanalyze a data set from a brain tumor treatment study.