Semiparametric Model for Semicompeting Risks Data with Application to Breast Cancer Study

• Activity Number: 187
• Type: Contributed
• Date/Time: Monday, August 10, 2015 : 10:30 AM to 12:20 PM
• Sponsor: Biometrics Section
• Author(s): Hong Zhu* and Renke Zhou and Melissa Bondy and Jing Ning
• Companies: The University of Texas Southwestern Medical Center and Baylor College of Medicine and Baylor College of Medicine and MD Anderson Cancer Center
• Keywords: Copula model ; Informative censoring ; Model diagnostic ; Semi-competing risks ; Simultaneous inference

Abstract

For many forms of cancer, patients will receive the initial regimen of treatments, then experience cancer progression and eventually die of the disease. Understanding the disease process in patients with cancer is essential in clinical, epidemiological and translational research. One challenge in analyzing such data is that death dependently censors cancer progression (e.g., recurrence), whereas progression does not censor death. We deal with the informative censoring by first selecting a suitable copula model through an exploratory diagnostic approach and then developing an inference procedure to simultaneously estimate the marginal survival function of cancer relapse and an association parameter in the copula model. We show that the proposed estimators possess consistency and weak convergence. We use simulation studies to evaluate the finite sample performance of the proposed method, and illustrate it through an application to data from a study of early stage breast cancer.