Confident Statistical Inference with Multiple Outcomes, Subgroups, and Other Issues of Multiplicity

Siyoen Kil (), Eloise Kaizar (), Szu-Yu Tang () and Jason C. Hsu ()
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Siyoen Kil: LSK Global Pharmaceutical Services
Eloise Kaizar: The Ohio State University
Szu-Yu Tang: Roche Tissue Diagnostics
Jason C. Hsu: The Ohio State University, Department of Statistics

Chapter 85 in Principles and Practice of Clinical Trials, 2022, pp 1659-1679 from Springer

Abstract: Abstract This chapter starts with a thorough discussion of different multiple comparison error rates, including weak and strong control for multiple tests and noncoverage probability for confidence sets. With multiple endpoints as an example, it describes which error rate controls would translate to incorrect decision rate controls. Then, using targeted therapy as the context, this chapter discusses a potential issue with some efficacy measures in terms of respecting logical relationships among the subgroups. A statistical principle that helps avoid this issue is described. As another example of multiplicity-induced issues to be aware of, it is shown that permutation test for patient targeting may not control Type I error rate in some situations. Finally, a list of the key points and a summary of the conclusions are given.

Keywords: Subgroups; Multiple comparisons; Prognostic effect; Permutation tests (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-319-52636-2_116

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DOI: 10.1007/978-3-319-52636-2_116

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