TY - JOUR
T1 - A general approach to categorizing a continuous scale according to an ordinal outcome
AU - Peng, Limin
AU - Manatunga, Amita
AU - Wang, Ming
AU - Guo, Ying
AU - Rahman, AKM Fazlur
N1 - Funding Information:
This research project was supported by grants from National Institute of Health ( R01MH079448 and R01HL113548 ). We thank Dr. Musselman for discussions related to Diabetes and Depression study.
Publisher Copyright:
© 2015 Elsevier B.V.
PY - 2016/5/1
Y1 - 2016/5/1
N2 - 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.
AB - 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.
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U2 - 10.1016/j.jspi.2015.12.006
DO - 10.1016/j.jspi.2015.12.006
M3 - Article
C2 - 26941475
AN - SCOPUS:84958773498
VL - 172
SP - 23
EP - 35
JO - Journal of Statistical Planning and Inference
JF - Journal of Statistical Planning and Inference
SN - 0378-3758
ER -