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Sensitivity analysis for missing data in regulatory submissions.

Thomas Permutt1

  • 1Division of Biometrics II, Offie of Biostatistics, Offie of Translational Sciences, Center for Drug Evaluation and Research, Silver Spring, MD, U.S.A.

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This summary is machine-generated.

The National Research Council recommends sensitivity analyses for clinical trials. This paper explores their recommendations for handling missing data in regulatory submissions and decision-making.

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Area of Science:

  • Biostatistics
  • Clinical Trials
  • Regulatory Science

Background:

  • The National Research Council Panel on Handling Missing Data in Clinical Trials issued recommendations for sensitivity analyses.
  • These recommendations have not been widely adopted in regulatory submissions.
  • Existing sensitivity analysis practices in regulatory settings differ from the NRC panel's suggestions.

Purpose of the Study:

  • To examine previous concepts of sensitivity analysis.
  • To explain how the NRC panel's recommendations differ from current practices.
  • To discuss the relevance of sensitivity analysis to decision-making for regulatory submissions.

Main Methods:

  • Literature review of sensitivity analysis concepts.
  • Comparative analysis of NRC recommendations and current regulatory practices.
  • Discussion of the role of sensitivity analysis in regulatory decision-making.

Main Results:

  • The NRC panel's recommendations offer a potentially improved approach to handling missing data in clinical trials.
  • A gap exists between the NRC's suggested methods and current regulatory implementation.
  • The role and importance of sensitivity analysis in regulatory decision-making require further clarification.

Conclusions:

  • The NRC panel's recommendations for sensitivity analysis in clinical trials are valuable but underutilized.
  • Further clarification and adoption of these methods are needed for robust regulatory decision-making regarding missing data.
  • Sensitivity analysis is crucial for both applicants and regulators in evaluating clinical trial findings.