This article proposes four new principles for logical biomarker cut-point selection methods to adhere to: subgroup sensibility, sensitivity, specificity, and target monotonicity. At every cut-point value, our method gives confidence intervals not only for the efficacy at that cut-point value, but also efficacies in the marker-positive and marker-negative subgroups defined by that cut-point. These confidence intervals are given simultaneously for all possible cut-point values. Using Alzheimer's disease (AD) and type 2 diabetes (T2DM) as examples, we show our method achieves the four principles. Our method strongly controls familywise type I error rate (FWER) across both levels of multiplicity: the multiplicity of having marker-positive and marker-negative subgroups at each cut-point, and the multiplicity of searching through infinitely many cut-points. This is in contrast to other available methods. The confidence level of our simultaneous confidence intervals is in fact exact (not conservative). An application (app) is available, which implements the method we propose.

中文翻译：

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用于逻辑选择生物标志物切点的精确同时置信区间

本文提出了四个新的逻辑生物标志物切点选择方法要遵守的原则：亚组敏感性、敏感性、特异性和目标单调性。在每个切点值处，我们的方法不仅给出了该切点值处的功效的置信区间，还给出了由该切点定义的标记阳性和标记阴性亚组的功效。对于所有可能的切点值，这些置信区间同时给出。以阿尔茨海默病 (AD) 和 2 型糖尿病 (T2DM) 为例，我们展示了我们的方法实现了四个原则。我们的方法强烈控制在两个多重性水平上的家庭类型 I 错误率 (FWER)：在每个切点处具有标记阳性和标记阴性子组的多重性，以及通过无限多个切点进行搜索的多重性。这与其他可用方法形成对比。我们同时置信区间的置信水平实际上是准确的（不是保守的）。一个应用程序（app）可用，它实现了我们提出的方法。