竞争风险:概念、方法和软件

IF 7.4 1区数学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS

Annual Review of Statistics and Its Application Pub Date : 2023-11-22 DOI:10.1146/annurev-statistics-040522-094556

Ronald B. Geskus

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引用次数: 0

摘要

竞争风险在事件时间数据分析中的作用日益得到承认。软件是现成的。然而，关于正确的分析仍然存在困惑:何时以及如何考虑竞争风险的存在?哪些数量与我的研究问题相关?如何估计它们，我需要做什么假设?竞争风险分析的主要量是原因特异性累积发生率、原因特异性危害和亚分布危害。我们描述了它们的非参数估计，概述了这些数量的回归模型，并解释了它们在解释上的差异。我们讨论了与学习问题类型相关的适当分析，我们建议使用R和Stata软件。我们的重点是医学研究中的竞争风险分析，但方法同样可以应用于其他领域，如社会科学、工程和经济学。预计《统计年鉴及其应用》第11卷的最终在线出版日期为2024年3月。修订后的估计数请参阅http://www.annualreviews.org/page/journal/pubdates。

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Competing Risks: Concepts, Methods, and Software

The role of competing risks in the analysis of time-to-event data is increasingly acknowledged. Software is readily available. However, confusion remains regarding the proper analysis: When and how do I need to take the presence of competing risks into account? Which quantities are relevant for my research question? How can they be estimated and what assumptions do I need to make? The main quantities in a competing risks analysis are the cause-specific cumulative incidence, the cause-specific hazard, and the subdistribution hazard. We describe their nonparametric estimation, give an overview of regression models for each of these quantities, and explain their difference in interpretation. We discuss the proper analysis in relation to the type of study question, and we suggest software in R and Stata. Our focus is on competing risks analysis in medical research, but methods can equally be applied in other fields like social science, engineering, and economics.Expected final online publication date for the Annual Review of Statistics and Its Application, Volume 11 is March 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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来源期刊

Annual Review of Statistics and Its Application MATHEMATICS, INTERDISCIPLINARY APPLICATIONS-STATISTICS & PROBABILITY

CiteScore

13.40

自引率

1.30%

发文量

期刊介绍： The Annual Review of Statistics and Its Application publishes comprehensive review articles focusing on methodological advancements in statistics and the utilization of computational tools facilitating these advancements. It is abstracted and indexed in Scopus, Science Citation Index Expanded, and Inspec.