A smooth nonparametric approach to determining cut-points of a continuous scale

• Type of Material: Article
• Authors: Zhiping Qiu, Emory University; Limin Peng, Emory University; Amita Manatunga, Emory University; Ying Guo, Emory University
• Language: English
• Date: 2019-06-01
• Publisher: Elsevier Science B.V.
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2018 Elsevier B.V. All rights reserved.
• License: Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1016/j.csda.2018.11.001
• Title of Journal or Parent Work: Computational Statistics & Data Analysis
• Volume: 134
• Start Page: 186
• End Page: 210
• Grant/Funding Information: Dr. Qiu’s work was also supported by the Education and Scientific Research Projects of Young and Middle-aged Teachers in Fujian Province, China (Grant No. JAT160027), the Natural Science Foundation of Fujian Province, China (Grant No. 2017J01002).; This research project was supported by grants from National Institute of Health (R01MH079448, R01HL113548 and R01MH105561).
• Keywords: Agreement; Model; Computer Science, Interdisciplinary Applications; Physical Sciences; Association; Technology; Nonparametric; Cut-point; Accuracy; Rank regression; Science & Technology; Selection; Computer Science; Depression; Optimization; Smoothing objective function; Statistics & Probability; Confidence intervals; Youden index; Mathematics; Optimal cutpoints; Mixture
• Research Categories: Biology, Biostatistics; Biology, Bioinformatics; Mathematics

Abstract

The problem of determining cut-points of a continuous scale according to an established 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.