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可视化分位数生存时间差异曲线。

Visualizing the quantile survival time difference curve.

机构信息

Section for Clinical Biometrics, Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria.

出版信息

J Eval Clin Pract. 2018 Aug;24(4):708-712. doi: 10.1111/jep.12948. Epub 2018 May 23.

Abstract

The difference between the pth quantiles of 2 survival functions can be used to compare patients' survival between 2 therapies. Setting p = 0.5 yields the median survival time difference. Varying p between 0 and 1 defines the quantile survival time difference curve which can be straightforwardly estimated by the horizontal differences between 2 Kaplan-Meier curves. The estimate's variability can be visualized by adding either a bundle of resampled bootstrap step functions or, alternatively, approximate bootstrap confidence bands. The user-friendly SAS software macro %kmdiff enables the straightforward application of this exploratory graphical approach. The macro is described, and its application is exemplified with breast cancer data. The advantages and limitations of the approach are discussed.

摘要

两种生存函数的第 p 个分位数的差异可用于比较两种治疗方法的患者生存情况。当 p=0.5 时,可得到中位生存时间差异。当 p 在 0 到 1 之间变化时,定义了分位数生存时间差异曲线,可通过两条 Kaplan-Meier 曲线之间的水平差异直接估计。可以通过添加一组重采样的自举分步函数或替代的近似自举置信带,直观地显示估计值的变异性。用户友好的 SAS 软件宏 %kmdiff 可用于直接应用这种探索性图形方法。本文介绍了该宏,并通过乳腺癌数据示例说明了其应用。讨论了该方法的优缺点。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/36f5/6099283/6d7b07ac5721/JEP-24-708-g001.jpg

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