文章基本信息

标题：STATISTICAL ANALYSIS OF SURVIVAL TIMES BASED ON PROPORTIONAL GENERALIZED ODDS MODELS
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作者：Xiaohu Li ; Linxiong Li ; Rui Fang 等
期刊名称：Journal of Data Science
印刷版ISSN：1680-743X
电子版ISSN：1683-8602
出版年度：2016
卷号：14
期号：4
页码：571-584
出版社：Tingmao Publish Company
摘要：In the area of survival analysis the most popular regression model is the Cox proportional hazards (PH) model. Unfortunately, in practice not all data sets satisfy the PH condition and thus the PH model cannot be used. To overcome the problem, the proportional odds (PO) model ( Pettitt 1982 and Bennett 1983a) and the generalized proportional odds (GPO) model ( Dabrowska and Doksum, 1988) were proposed, which can be considered in some sense generalizations of the PH model. However, there are examples indicating that the use of the PO or GPO model is not appropriate. As a consequence, a more general model must be considered. In this paper, a new model, called the proportional generalized odds (PGO) model, is introduced, which covers PO and GPO models as special cases. Estimation of the regression parameters as well as the underlying survival function of the GPO model is discussed. An application of the model to a data set is presented.
关键词：Burr distribution; Frailty parameter; Maximum likelihood estimation; Censored data.