文章基本信息

标题：Transformation Models for Survival Data Analysis with Applications
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作者：Yang Liu ; Qiusheng Chen ; Xufeng Niu 等
期刊名称：Open Journal of Statistics
印刷版ISSN：2161-718X
电子版ISSN：2161-7198
出版年度：2016
卷号：06
期号：01
页码：133-155
DOI：10.4236/ojs.2016.61013
语种：English
出版社：Scientific Research Publishing
摘要：When the event of interest never occurs for a proportion of subjects during the study period, survival models with a cure fraction are more appropriate in analyzing this type of data. Considering the non-linear relationship between response variable and covariates, we propose a class of generalized transformation models motivated by Zeng et al. [1] transformed proportional time cure model, in which fractional polynomials are used instead of the simple linear combination of the covariates. Statistical properties of the proposed models are investigated, including identifiability of the parameters, asymptotic consistency, and asymptotic normality of the estimated regression coefficients. A simulation study is carried out to examine the performance of the power selection procedure. The generalized transformation cure rate models are applied to the First National Health and Nutrition Examination Survey Epidemiologic Follow-up Study (NHANES1) for the purpose of examining the relationship between survival time of patients and several risk factors.
关键词：Link Functions;Mixture Cure Rate Models;Noninformative Improper Priors;Proportional Hazards Models;Proportional Odds Models