Semiparametric model for semi-competing risks data with application to breast cancer study

Renke Zhou (), Hong Zhu (), Melissa Bondy () and Jing Ning ()
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Renke Zhou: Baylor College of Medicine
Hong Zhu: The University of Texas Southwestern Medical Center
Melissa Bondy: Baylor College of Medicine
Jing Ning: The University of Texas MD Anderson Cancer Center

Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, 2016, vol. 22, issue 3, No 7, 456-471

Abstract: 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.

Keywords: Copula model; Informative censoring; Model diagnostic; Semi-competing risks; Simultaneous inference (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1007/s10985-015-9344-x

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