Quantile Regression Based On A Weighted Approach Under Semi-Competing Risks Data

• Main Authors: Hsiao, Ming-Fu, 蕭銘富
• Other Authors: Hsieh, Jin-Jian
• Format: Others
• Language: en_US
• Published: 2012
• Online Access: http://ndltd.ncl.edu.tw/handle/11502551139030846017

碩士 === 國立中正大學 === 數理統計研究所 === 100 === In this article, we investigate the quantile regression analysis for semi-competing risks data. Since the non-terminal event time is dependently cnesored by the terminal event time, it is difficult to estimate the quantile regression coefficients without extra assumptions. With AC (Archimedean copula) model assumption assumption, Hsieh et al. (2011) proposed a method by ``IPCW" technique to estimate the parameters. Portnoy (2003) considered the quantile regression model under right censoring data. We extend his approach to construct a weight function, and then impose the weight function to estimate the quantile regerssion parameters for semi-competing risks data. We also prove the consistency and asymptotic properties for the proposed estimator. According to the simulation studies, the performance of our proposed method is well. We also apply our suggested approach to analyze a real data, which is Bone Marrow Transplant data.