解读决策曲线分析的简易分步指南。
A simple, step-by-step guide to interpreting decision curve analysis.
作者信息
Vickers Andrew J, van Calster Ben, Steyerberg Ewout W
机构信息
1Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, 485 Lexington Avenue, 2nd Floor, New York, NY 10017 USA.
2Department of Development and Regeneration, KU Leuven, Oude Markt 13, 3000 Leuven, Belgium.
出版信息
Diagn Progn Res. 2019 Oct 4;3:18. doi: 10.1186/s41512-019-0064-7. eCollection 2019.
BACKGROUND
Decision curve analysis is a method to evaluate prediction models and diagnostic tests that was introduced in a 2006 publication. Decision curves are now commonly reported in the literature, but there remains widespread misunderstanding of and confusion about what they mean.
SUMMARY OF COMMENTARY
In this paper, we present a didactic, step-by-step introduction to interpreting a decision curve analysis and answer some common questions about the method. We argue that many of the difficulties with interpreting decision curves can be solved by relabeling the -axis as "benefit" and the -axis as "preference." A model or test can be recommended for clinical use if it has the highest level of benefit across a range of clinically reasonable preferences.
CONCLUSION
Decision curves are readily interpretable if readers and authors follow a few simple guidelines.
背景
决策曲线分析是一种评估预测模型和诊断测试的方法，于2006年发表。决策曲线如今在文献中普遍被报道，但对于其含义仍存在广泛的误解和困惑。
评论总结
在本文中，我们提供了一份教学式的、逐步解读决策曲线分析的指南，并回答了关于该方法的一些常见问题。我们认为，通过将x轴重新标记为“益处”，y轴重新标记为“偏好”，可以解决许多解读决策曲线的困难。如果一个模型或测试在一系列临床合理的偏好范围内具有最高水平的益处，那么就可以推荐其用于临床。
结论
如果读者和作者遵循一些简单的指南，决策曲线是易于解读的。
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