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生存分析在呼吸健康研究中的时间事件数据。

Survival analysis of time-to-event data in respiratory health research studies.

机构信息

Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Victoria, Australia; Victorian Centre for Biostatistics (ViCBiostat), Melbourne, Victoria, Australia.

出版信息

Respirology. 2014 May;19(4):483-92. doi: 10.1111/resp.12281. Epub 2014 Apr 1.

Abstract

This article provides a review of techniques for the analysis of survival data arising from respiratory health studies. Popular techniques such as the Kaplan-Meier survival plot and the Cox proportional hazards model are presented and illustrated using data from a lung cancer study. Advanced issues are also discussed, including parametric proportional hazards models, accelerated failure time models, time-varying explanatory variables, simultaneous analysis of multiple types of outcome events and the restricted mean survival time, a novel measure of the effect of treatment.

摘要

本文回顾了源于呼吸系统健康研究的生存数据分析技术。介绍了Kaplan-Meier 生存图和 Cox 比例风险模型等常用技术,并使用肺癌研究数据进行了说明。还讨论了一些高级问题,包括参数比例风险模型、加速失效时间模型、时变解释变量、多种结局事件的同时分析以及治疗效果的新度量——限制平均生存时间。

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