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Hazard ratio funnel plots for survival comparisons

机译:危险比漏斗图用于生存比较

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摘要

Background: Funnel plots are a form of control chart that give a snapshot of many institutions at a particular moment in time. This paper describes how funnel plots may be constructed for survival analyses based on hazard ratios obtained from a Cox regression model with adjustment for covariates and allowance for overdisper-sion.rnMethod: Analysis of simulated and real survival data. Results: It describes how centred hazard ratio estimates adjusted for covariates can be obtained from a Cox regression and gives details of the necessary programming in Stata. Allowance for overdispersion can be made by multiplying the standard errors by a factor based on either the model or the log-rank χ~2 statistics. Simulated results and a real example are presented. Conclusion: Funnel plots based on hazard ratios are easier to interpret than multiple Kaplan-Meier survival plots, and in contrast to funnel plots based on survival at, say, 5 years, are less open to accusations of bias and use more information. The interpretation of such plots may be enhanced by using standard meta-analysis methods. Hazard ratio comparisons may now be added to the repertoire of techniques used by Cancer Registries, Primary Care Trusts, and other commissioners of healthcare.
机译:背景:漏斗图是控制图的一种形式,可以在特定时间提供许多机构的快照。本文描述了如何基于从Cox回归模型获得的危险比构建漏斗图以进行生存分析,并通过对协变量进行调整并允许过度分散进行分析。方法:模拟和真实生存数据的分析。结果:它描述了如何从Cox回归中获得针对协变量调整的中心风险比估计值,并详细介绍了Stata中必需的编程。可以通过将标准误差乘以基于模型或对数秩χ〜2统计量的因子来获得超分散的余量。给出了仿真结果和一个真实的例子。结论:基于风险比的漏斗图比多个Kaplan-Meier生存图更易于解释,并且与基于5年生存率的漏斗图相比,它不容易受到偏见的指责,并且可以使用更多信息。可以通过使用标准的荟萃分析方法来增强此类图的解释。现在可以将危险比的比较添加到癌症登记处,初级保健基金会和其他医疗保健专员所使用的技术中。

著录项

  • 来源
    《Journal of Epidemiology & Community Health》 |2009年第10期|856-861|共6页
  • 作者

    P Silcocks;

  • 作者单位

    Trent Cancer Registry, and the NIHR Research Design Service for the East Midlands, Nottingham, UK Trent Cancer Registry, 5 Old Fulwood Road, Sheffield, S10 3TG, UK;

  • 收录信息 美国《科学引文索引》(SCI);美国《化学文摘》(CA);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

  • 入库时间 2022-08-18 01:09:43

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