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

Limin Peng, Amita Manatunga, Ming Wang, Ying Guo, AKM Fazlur Rahman

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)23-35
Number of pages13
JournalJournal of Statistical Planning and Inference
Volume172
DOIs
StatePublished - May 1 2016

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty
  • Applied Mathematics

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