诊断医学统计学

P. Schlattmann
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引用次数: 5

摘要

本教程介绍了诊断医学的统计方法。诊断测试的有效性可以用已知参考或金标准的二元诊断测试定义的敏感性和特异性来评估。以降钙素原(cut off value≥0.5 g/L)为例,以脓毒症-2标准作为脓毒症诊断的参考标准。接下来,引入了似然比,它结合了灵敏度和特异性给出的信息。对于这些测度,给出了置信区间的构造方法。然后,利用贝叶斯定理引入预测值。预测值有时很难传达。这可以使用应用于我们的例子的固有频率来改善。降钙素原实际上是一个连续的生物标志物,因此我们引入了接收算子曲线(ROC)和曲线下面积(AUC)的使用。最后我们讨论了诊断研究的样本量估计。为了展示如何在实践中应用这些概念,我们解释了如何使用免费的软件R。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Statistics in diagnostic medicine
Abstract This tutorial gives an introduction into statistical methods for diagnostic medicine. The validity of a diagnostic test can be assessed using sensitivity and specificity which are defined for a binary diagnostic test with known reference or gold standard. As an example we use Procalcitonin with a cut off value ≥ 0.5 g/L as a test and Sepsis-2 criteria as a reference standard for the diagnosis of sepsis. Next likelihood ratios are introduced which combine the information given by sensitivity and specificity. For these measures the construction of confidence intervals is demonstrated. Then, we introduce predictive values using Bayes’ theorem. Predictive values are sometimes difficult to communicate. This can be improved using natural frequencies which are applied to our example. Procalcitonin is actually a continuous biomarker, hence we introduce the use of receiver operator curves (ROC) and the area under the curve (AUC). Finally we discuss sample size estimation for diagnostic studies. In order to show how to apply these concepts in practice we explain how to use the freely available software R.
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