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Multiple Statistical Inferences

  • Ton J. Cleophas
  • Aeilko H. Zwinderman
  • Toine F. Cleophas

Abstract

Clinical trials often assess the efficacy of more than one new treatment and often use many efficacy variables. Also, after overall testing these efficacy variables, additional questions about subgroups differences or about what variables do or do not contribute to the efficacy results, remain. Assessment of such questions introduces the statistical problem of multiple comparison and multiple testing, which increases the risk of false positive statistical results, and thus increases the type-I error risk. In this chapter simple methods are discussed which can help to control this risk.

Keywords

Composite Variable Primary Variable Honestly Significant Difference Efficacy Variable Endpoint Variable 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media Dordrecht 2006

Authors and Affiliations

  • Ton J. Cleophas
    • 1
    • 2
  • Aeilko H. Zwinderman
    • 3
  • Toine F. Cleophas
    • 4
  1. 1.European Interuniversity College of Pharmaceutical Medicine LyonFrance
  2. 2.Department MedicineAlbert Schweitzer HospitalDordrechtThe Netherlands
  3. 3.Department Biostatistics and EpidemiologyAcademic Medical Center AmsterdamThe Netherlands
  4. 4.Technical UniversityDelftThe Netherlands

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