Step-up testing procedure for multiple comparisons with a control for a latent variable model with ordered categorical responses
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AbstractIn clinical studies, multiple comparisons of several treatments to a control with ordered categorical responses are often encountered. A popular statistical approach to analyzing the data is to use the logistic regression model with the proportional odds assumption. As discussed in several recent research papers, if the proportional odds assumption fails to hold, the undesirable consequence of an inflated familywise type I error rate may affect the validity of the clinical findings. To remedy the problem, a more flexible approach that uses the latent normal model with single-step and stepwise testing procedures has been recently proposed. In this paper, we introduce a step-up procedure that uses the correlation structure of test statistics under the latent normal model. A simulation study demonstrates the superiority of the proposed procedure to all existing testing procedures. Based on the proposed step-up procedure, we derive an algorithm that enables the determination of the total sample size and the sample size allocation scheme with a pre-determined level of test power before the onset of a clinical trial. A clinical example is presented to illustrate our proposed method. Copyright (c) 2014 John Wiley & Sons, Ltd.
All Author(s) ListLin YQ, Kwong KS, Cheung SH, Poon WY
Journal nameStatistics in Medicine
Year2014
Month9
Day20
Volume Number33
Issue Number21
PublisherWiley: 12 months
Pages3629 - 3638
ISSN0277-6715
eISSN1097-0258
LanguagesEnglish-United Kingdom
Keywordsfamilywise error rate; latent normal variable model; ordered categorical response; sample size determination
Web of Science Subject CategoriesMathematical & Computational Biology; MATHEMATICAL & COMPUTATIONAL BIOLOGY; Mathematics; Medical Informatics; MEDICAL INFORMATICS; Medicine, Research & Experimental; MEDICINE, RESEARCH & EXPERIMENTAL; Public, Environmental & Occupational Health; PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH, SCI; Research & Experimental Medicine; Statistics & Probability; STATISTICS & PROBABILITY

Last updated on 2021-13-01 at 00:10