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Related Experiment Videos

Procedures for two-sample comparisons with multiple endpoints controlling the experimentwise error rate.

W Lehmacher1, G Wassmer, P Reitmeir

  • 1Gesellschaft für Strahlen- und Umweltforschung München, Institut für Medizinische Informatik und Systemforschung, Neuherberg, Germany.

Biometrics
|June 1, 1991
PubMed
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This study introduces a new closed multiple test procedure for clinical trials with multiple endpoints. It offers a powerful way to detect global treatment differences and identify specific endpoints responsible for these effects.

Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Statistical Inference

Background:

  • Clinical trials frequently compare two treatment groups using multiple endpoints.
  • Traditional methods like the T2 test and Bonferroni correction have limitations.
  • Multivariate methods, such as O'Brien's tests, are powerful for detecting global differences when treatment effects are unidirectional but do not pinpoint specific endpoint contributions.

Purpose of the Study:

  • To develop a statistical procedure for analyzing clinical trials with multiple endpoints.
  • To address the limitation of existing multivariate methods by identifying specific endpoints driving significant global differences.
  • To construct a closed multiple test procedure that controls the experimentwise error rate.

Main Methods:

Related Experiment Videos

  • Utilized statistics based on simple or weighted sums of single endpoints, as proposed by O'Brien (1984).
  • Developed a closed multiple test procedure for sequential testing of hypotheses.
  • The procedure involves testing the global hypothesis, followed by lower-dimensional marginal hypotheses, and finally single hypotheses.

    • Main Results:

    • The proposed closed multiple test procedure effectively controls the experimentwise error rate.
    • This procedure maintains the power of multivariate tests for detecting global differences.
    • Crucially, it enables the identification of significant differences among specific endpoints or sets of endpoints.

    Conclusions:

    • The developed closed multiple test procedure offers a powerful and informative approach for clinical trials with multiple endpoints.
    • It overcomes the limitations of traditional multivariate methods by providing specific endpoint-level insights.
    • This method enhances clinical trial analysis by balancing the detection of overall treatment effects with the identification of key contributing factors.