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

  • Siyoen Kil
  • Eloise Kaizar
  • Szu-Yu Tang
  • Jason C. HsuEmail author
Living reference work entry

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 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Siyoen Kil
    • 1
  • Eloise Kaizar
    • 2
  • Szu-Yu Tang
    • 3
  • Jason C. Hsu
    • 4
    Email author
  1. 1.LSK Global Pharmaceutical ServicesSeoulRepublic of Korea
  2. 2.The Ohio State UniversityColumbusUSA
  3. 3.Roche Tissue DiagnosticsOro ValleyUSA
  4. 4.Department of StatisticsThe Ohio State UniversityColumbusUSA

Section editors and affiliations

  • Stephen George
    • 1
  1. 1.Dept. of Biostatistics and Bioinformatics,Basic Science DivisonDuke University, School of MedicineDurhamUSA

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