標題: Regression analysis based on semicompeting risks data
作者: Hsieh, Jin-Jian
Wang, Weijing
Ding, A. Adam
統計學研究所
Institute of Statistics
關鍵字: copula model;dependent censoring;model selection;multiple events data;transformation model
公開日期: 2008
摘要: Semicompeting risks data are commonly seen in biomedical applications in which a terminal event censors a non-terminal event. Possible dependent censoring complicates statistical analysis. We consider regression analysis based on a non-terminal event, say disease progression, which is subject to censoring by death. The methodology proposed is developed for discrete covariates under two types of assumption. First, separate copula models are assumed for each covariate group and then a flexible regression model is imposed on the progression time which is of major interest. Model checking procedures are also proposed to help to choose a best-fitted model. Under a two-sample setting, Lin and co-workers proposed a competing method which requires an additional marginal assumption on the terminal event and implicitly assumes that the dependence structures in the two groups are the same. Using simulations, we compare the two approaches on the basis of their finite sample performances and robustness properties under model misspecification. The method proposed is applied to a bone marrow transplant data set.
URI: http://hdl.handle.net/11536/9799
ISSN: 1369-7412
期刊: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
Volume: 70
起始頁: 3
結束頁: 20
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