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Author Notes:

Corresponding author. Tel.: +1 404 727 7701. lpeng@emory.edu (L. Peng).

We thank Dr. Musselman for discussions related to Diabetes and Depression study.

Subjects:

Research Funding:

This research project was supported by grants from National Institute of Health (R01MH079448 and R01HL113548).

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

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

Tools:

Journal Title:

Journal of Statistical Planning and Inference

Volume:

Volume 172

Publisher:

, Pages 23-35

Type of Work:

Article | Post-print: After Peer Review

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.

Copyright information:

© 2015 Elsevier B.V.

This is an Open Access work distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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