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Panel forum on multiple comparison procedures: a commentary from a complex trial design and analysis plan.

Sue-Jane Wang1, Frank Bretz, Alex Dmitrienko

  • 1U.S. Food and Drug Administration, HFD-700, WO 21, MailStop Room 3562, 10903 New Hampshire Avenue, Silver Spring, MD 20993, USA. suejane.wang@fda.hhs.gov

Biometrical Journal. Biometrische Zeitschrift
|April 5, 2013
PubMed
Summary

Navigating multiplicity in clinical trials is complex. This forum discussed challenges beyond single studies, focusing on program-level analysis for regulatory approval and scientific reporting.

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Regulatory Science

Background:

  • Confirmatory trials typically control Type I error rates at the studywise or familywise level.
  • Complex study designs, such as multi-regional Phase III programs, introduce novel multiplicity challenges.
  • Existing statistical methods may not adequately address multiplicity across multiple studies and endpoints.

Purpose of the Study:

  • To discuss the multiplicity problem in complex clinical trial programs.
  • To explore criteria for multiplicity control beyond single confirmatory trials.
  • To address challenges in reporting findings when regulatory outcomes differ.

Main Methods:

  • A panel forum with academic, industry, and regulatory statistical scientists.
  • Discussion centered on a case example of a Phase III program with two identical multiregional trials.
  • Panelists reviewed a sophisticated multiplicity problem involving multiple endpoints, doses, regions, and a protocol amendment.

Main Results:

  • Panelists identified significant challenges in pooling data and defining multiplicity across studies.
  • Differences in professional perspectives highlighted the complexity of the issues.
  • No single consensus was reached, underscoring the need for novel approaches.

Conclusions:

  • There is a need for novel Multiple Comparison Procedures (MCP) that extend beyond individual study levels.
  • Addressing experimentwise and programwise error rates is crucial for complex, multi-study programs.
  • Future research in MCP is expected to face significant scientific and regulatory challenges.