The problem of determining cut-points of a continuous scale according to an establish categorical scale is often encountered in practice for the purposes such as making diagnosis or treatment recommendation, determining study eligibility, or facilitating interpretations. A general analytic framework was recently proposed for assessing optimal cut-points defined based on some pre-specified criteria. However, the implementation of the existing nonparametric estimators under this framework and the associated inferences can be computationally intensive when more than a few cut-points need to be determined. To address this important issue, a smoothing-based modification of the current method is proposed and is found to substantially improve the computational speed as well as the asymptotic convergence rate. Moreover, a plug-in type variance estimation procedure is developed to further facilitate the computation. Extensive simulation studies confirm the theoretical results and demonstrate the computational benefits of the proposed method. The practical utility of the new approach is illustrated by an application to a mental health study.

中文翻译：

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一种确定连续尺度切点的平滑非参数方法

在实践中经常遇到根据建立的分类量表确定连续量表的切点的问题，例如提出诊断或治疗建议、确定研究资格或促进解释。最近提出了一个通用分析框架，用于评估基于一些预先指定的标准定义的最佳切点。然而，当需要确定多个分界点时，在此框架下实施现有的非参数估计器和相关的推论可能是计算密集型的。为了解决这个重要问题，提出了对当前方法的基于平滑的修改，并且发现可以显着提高计算速度和渐近收敛速度。而且，开发了插件式方差估计程序以进一步促进计算。广泛的模拟研究证实了理论结果并证明了所提出方法的计算优势。新方法的实际效用通过在心理健康研究中的应用得到了说明。