Multiple Statistical Inferences

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 the previous chapter six commonly-used methods for controlling the risk of this problem have been addressed. This chapter gives a more mathematical approach of the problem, and gives examples in which different methods are compared with one another.


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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Waldinger MD, Hengeveld MW, Zwinderman AH, Olivier B (1998). Effect of SSRI antidepressants on ejaculation: A double-blind, randomized, placebo-controlled study with fluoxetine, fluvoxamine, paroxetine, and sertraline. Journal of Clinical Psychopharmacology, 18 (4): 274–281.CrossRefGoogle Scholar
  2. 2.
    Multiple comparisons boek, Edition University of Leiden , Neth, 1999.Google Scholar
  3. 3.
    SAS Statistical Software, 1998.Google Scholar
  4. 4.
    SPSS Statistical Software, Chicago, IL, 1996.Google Scholar
  5. 5.
    Hochberg Y. A sharper Bonferroni procedure for multiple tests of significance. Biometrika 1988, 75: 800–802.MATHCrossRefMathSciNetGoogle Scholar
  6. 6.
    Fuchs HA. The use of the disease activity score in the analysis of clinical trials in rheumatoid arthritis. J Rheumatol, 1993, 20(11): 1863–6.Google Scholar
  7. 7.
    Lauter J. Exact t and F-tests for analyzing studies with multiple endpoints. Biometrics 1996, 52: 964–970.CrossRefMathSciNetGoogle Scholar
  8. 8.
    Jukema JW, Bruschke AV, Van Boven AJ, Zwinderman AH, et al. Effects of lipid lowering by pravastatin on the regression of coronary artery disease in symptomatic men. Circulation 1995; 91: 2528–40.Google Scholar

Copyright information

© Springer Science + Business Media B.V. 2009

Personalised recommendations