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混合和促进时间治愈率模型的变量选择。

Variable selection for mixture and promotion time cure rate models.

机构信息

1 Department of Biostatistics, Indiana University School of Medicine, Indianapolis, IN, USA.

2 Department of Bioinformatics and Biostatistics, Shanghai Jiaotong University, Shanghai, China.

出版信息

Stat Methods Med Res. 2018 Jul;27(7):2185-2199. doi: 10.1177/0962280216677748. Epub 2016 Nov 16.

Abstract

Failure-time data with cured patients are common in clinical studies. Data from these studies are typically analyzed with cure rate models. Variable selection methods have not been well developed for cure rate models. In this research, we propose two least absolute shrinkage and selection operators based methods, for variable selection in mixture and promotion time cure models with parametric or nonparametric baseline hazards. We conduct an extensive simulation study to assess the operating characteristics of the proposed methods. We illustrate the use of the methods using data from a study of childhood wheezing.

摘要

在临床研究中,带有治愈患者的失效时间数据很常见。这些研究中的数据通常使用治愈率模型进行分析。对于治愈率模型,尚未很好地开发出变量选择方法。在这项研究中,我们提出了两种基于最小绝对收缩和选择算子的方法,用于具有参数或非参数基线风险的混合和促进时间治愈模型中的变量选择。我们进行了广泛的模拟研究来评估所提出方法的操作特性。我们使用来自儿童喘息研究的数据来说明这些方法的使用。

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