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促进治疗模型估计和选择中的符号一致性。

Promoting sign consistency in the cure model estimation and selection.

机构信息

Department of Statistics, Nanjing University of Finance and Economics, Nanjing, Jiangsu, China.

Department of Biostatistics, Yale University, New Haven, CT, USA.

出版信息

Stat Methods Med Res. 2020 Jan;29(1):15-28. doi: 10.1177/0962280218820356. Epub 2019 Jan 2.

Abstract

In survival analysis, when a subset of subjects has extremely long survival, the two-part cure rate model has been commonly adopted. In the two-part model, the first part is for a binary response and describes the probability of cure. The second part is for a survival response and describes the probability of survival. Despite their intuitive interconnections, most of the existing works estimate the two parts without any constraint. The existing works on proportionality promote similarity in magnitudes (i.e. quantitative similarity) and can be too restrictive. In this study, for the two-part cure rate model, we propose imposing a sign-based penalty to promote similarity in signs (i.e. qualitative similarity). The proposed strategy can be more informative than those that neglect the two-part interconnections and be less restrictive than the existing proportionality works. Penalty is also imposed to select relevant variables and accommodate high-dimensional data. Numerical studies, including simulation and two data analyses, demonstrate the advantageous performance of the proposed approach.

摘要

在生存分析中,当一部分受试者的生存时间极长时,通常采用两部分治愈率模型。在两部分模型中,第一部分用于二项响应,并描述治愈的概率。第二部分用于生存响应,并描述生存的概率。尽管它们具有直观的联系,但大多数现有工作在没有任何约束的情况下估计两部分。关于比例性的现有工作促进了大小上的相似性(即定量相似性),并且可能过于严格。在这项研究中,对于两部分治愈率模型,我们提出施加基于符号的惩罚来促进符号上的相似性(即定性相似性)。与忽略两部分关联的策略相比,所提出的策略更具信息量,并且比现有比例性工作的限制更小。还施加了惩罚来选择相关变量并适应高维数据。包括模拟和两个数据分析在内的数值研究证明了所提出方法的优越性能。

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