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

Lifetime Data Analysis
Renke ZhouJing Ning

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.

References

Jun 9, 2005·Lifetime Data Analysis·Min-Chi Chen, Karen Bandeen-Roche
Apr 24, 2007·Biometrics·Limin Peng, Jason P Fine
Sep 6, 2007·Journal of Clinical Oncology : Official Journal of the American Society of Clinical Oncology·Abenaa M BrewsterMelissa L Bondy
Mar 13, 2012·Lifetime Data Analysis·Jin-Jian Hsieh, Yu-Ting Huang
Jan 9, 2014·CA: a Cancer Journal for Clinicians·Rebecca SiegelAhmedin Jemal

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Citations

Aug 23, 2019·Journal of the Royal Statistical Society. Series B, Statistical Methodology·Jin PiaoYu Shen

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