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Noninferiority testing for matched‐pair ordinal data with misclassification
Author(s) -
Han Yuanyuan,
Lu ZhaoHua,
Poon WaiYin
Publication year - 2019
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.8364
Subject(s) - categorical variable , ordinal data , computer science , ordinal scale , statistics , type i and type ii errors , data mining , ordinal regression , econometrics , mathematics , machine learning
New treatments that are noninferior or equivalent to—but not necessarily superior to—the reference treatment may still be beneficial to patients because they have fewer side effects, are more convenient, take less time, or cost less. The noninferiority test is widely used in medical research to provide guidance in such situation. In addition, categorical variables are frequently encountered in medical research, such as in studies involving patient‐reported outcomes. In this paper, we develop a noninferiority testing procedure for correlated ordinal categorical variables based on a paired design with a latent normal distribution approach. Misclassification is frequently encountered in the collection of ordinal categorical data; therefore, we further extend the procedure to account for misclassification using information in the partially validated data. Simulation studies are conducted to investigate the accuracy of the estimates, the type I error rates, and the power of the proposed procedure. Finally, we analyze one substantive example to demonstrate the utility of the proposed approach.