In practice, disease outcomes are often measured in a continuous scale, and classification of subjects into meaningful disease categories is of substantive interest. To address this problem, we propose a general analytic framework for determining cut-points of the continuous scale. We develop a unified approach to assessing optimal cut-points based on various criteria, including common agreement and association measures. We study the nonparametric estimation of optimal cut-points. Our investigation reveals that the proposed estimator, though it has been ad-hocly used in practice, pertains to nonstandard asymptotic theory and warrants modifications to traditional inferential procedures. The techniques developed in this work are generally adaptable to study other estimators that are maximizers of nonsmooth objective functions while not belonging to the paradigm of M-estimation. We conduct extensive simulations to evaluate the proposed method and confirm the derived theoretical results. The new method is illustrated by an application to a mental health study.

中文翻译：

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根据顺序结果对连续量表进行分类的通用方法


在实践中，疾病结果通常以连续的尺度来衡量，并且将受试者分类为有意义的疾病类别具有实质性意义。为了解决这个问题，我们提出了一个用于确定连续尺度的切点的通用分析框架。我们开发了一种统一的方法来根据各种标准评估最佳切点，包括共同协议和关联措施。我们研究最佳切点的非参数估计。我们的调查表明，所提出的估计量虽然在实践中被临时使用，但属于非标准渐近理论，并且需要对传统推理过程进行修改。这项工作中开发的技术通常适用于研究其他估计器，这些估计器是非光滑目标函数的最大化但不属于 M 估计范式。我们进行了广泛的模拟来评估所提出的方法并确认所得出的理论结果。通过在心理健康研究中的应用来说明新方法。