Statistical Analysis for Recurrence Events Data with Multiple Competing Risks

• Main Authors: Ni, Chia-Jung, 倪佳蓉
• Other Authors: Wang, Wei-Jing
• Format: Others
• Language: en_US
• Published: 2016
• Online Access: http://ndltd.ncl.edu.tw/handle/97905494787963653642

碩士 === 國立交通大學 === 統計學研究所 === 104 === In longitudinal follow-up studies, recurrent events data in presence of competing risks are commonly seen. Besides the end-of-study effect, subjects may leave the study due to different reasons such as loss to follow-up, withdrawal or the occurrence of terminal events. The complicated mechanism s as well as the censoring issue becomes the major challenges for statistical inference. We study the complicated phenomenon under a unified framework which includes some familiar data structures as special cases. We also introduce the frailty model which is a popular and useful approach to constructing correlated random variables. Then we propose several data generation algorithms and apply them in our simulation study. Specifically we consider estimation of the cumulative incidence function (CIF), which is useful descriptive measure for describing competing risks data, when the data are generated according to the proposed algorithms.