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

In: Principles and Practice of Clinical Trials

Author

Listed:
  • 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)

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.

Suggested Citation

  • Siyoen Kil & Eloise Kaizar & Szu-Yu Tang & Jason C. Hsu, 2022. "Confident Statistical Inference with Multiple Outcomes, Subgroups, and Other Issues of Multiplicity," Springer Books, in: Steven Piantadosi & Curtis L. Meinert (ed.), Principles and Practice of Clinical Trials, chapter 85, pages 1659-1679, Springer.
    • Handle: RePEc:spr:sprchp:978-3-319-52636-2_116
    DOI: 10.1007/978-3-319-52636-2_116
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