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Composite and multicomponent end points in clinical trials.

Abdul J Sankoh1, Haihong Li1, Ralph B D'Agostino2

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Statistics in Medicine
|July 5, 2017
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Summary

This commentary clarifies multiplicity problems in clinical trials arising from composite endpoints. It discusses when to adjust for multiple testing and relevant statistical methods for FDA guidance.

Keywords:
composite end pointmulticomponent end pointmultiple end pointmultiple testing

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Area of Science:

  • Clinical Trials Methodology
  • Biostatistics
  • Regulatory Science

Background:

  • The U.S. Food and Drug Administration (FDA) issued draft guidance in January 2017 concerning multiple endpoints in clinical trials.
  • Composite and multicomponent endpoints present unique statistical challenges related to multiplicity.
  • Understanding these challenges is crucial for designing robust clinical trials.

Purpose of the Study:

  • To clarify the statistical problems associated with multiplicity when analyzing components of composite or multicomponent endpoints.
  • To provide guidance on the appropriate application of multiplicity adjustments in clinical trial analysis.
  • To discuss relevant multiple testing methodologies for regulatory submissions.

Main Methods:

  • This work is a commentary and discussion, not an empirical study.
  • It reviews existing statistical principles and FDA draft guidance.
  • Key concepts discussed include composite endpoints, multiplicity, and statistical adjustment methods.

Main Results:

  • Composite endpoints can lead to a class of multiplicity problems when individual components are analyzed separately.
  • Multiplicity adjustments are necessary in specific situations to maintain the integrity of trial results and control Type I error rates.
  • The commentary outlines considerations for selecting and applying appropriate multiple testing procedures.

Conclusions:

  • Careful consideration of endpoint composition and statistical analysis is essential for valid clinical trial interpretation.
  • Adherence to multiplicity adjustment principles ensures the reliability of findings for regulatory decision-making.
  • This discussion aims to enhance understanding and application of statistical methods for complex endpoints in drug development.