A general approach to categorizing a continuous scale according to an ordinal outcome

• Authors: Limin Peng, Emory University; Amita Manatunga, Emory University; Ming Wang, Penn State University; Ying Guo, Emory University; AKM Fazlur Rahman, Emory University
• Journal Title: Journal of Statistical Planning and Inference
• Volume: Volume 172
• Publisher: Elsevier | 2016-05-01, Pages 23-35
• Type of Work: Article | Post-print: After Peer Review
• Publisher DOI: http://doi.org/10.1016/j.jspi.2015.12.006
• Subjects: Health Sciences, Public Health; Health Sciences, General
• Keywords: Science & Technology; Physical Sciences; Statistics & Probability; Mathematics; Agreement; Association; Empirical process; M-estimation; Non-smooth objective function; Subsampling; CUBE ROOT ASYMPTOTICS; INTERNATIONAL NEUROPSYCHIATRIC INTERVIEW; DSM-IV; BOOTSTRAP; DEPRESSION; POINT; ESTIMATOR; MIXTURE; MINI

Abstract

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.