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Published on: September 20, 2019
A reviewer's perspective on multiple endpoint issues in clinical trials
1Division of Biometrics III, Food and Drug Administration, Rockville, Maryland 20857, USA.
Journal of Biopharmaceutical Statistics
|November 14, 1997
Summary
Controlling statistical significance in clinical trials with multiple endpoints is crucial to avoid approving ineffective treatments. This study explores methods to manage type 1 error inflation, ensuring reliable treatment effect claims.
Area of Science:
- Clinical Trials
- Biostatistics
- Statistical Significance
Background:
- Multiple endpoints in clinical trials can inflate the probability of a Type 1 error, potentially leading to the approval of ineffective therapies.
- Univariate significance testing for each endpoint individually, or ignoring multiplicity, compromises the reliability of treatment effect claims.
- Controlling Type 1 error probability at a prespecified alpha-level is essential while maintaining high statistical power to detect meaningful treatment effects.
Purpose of the Study:
- To provide a clinical and statistical background on multiplicity issues in clinical trials.
- To illustrate the impact of ignoring multiplicity on Type 1 error probability through simulation results.
- To discuss global methods for controlling Type 1 error and introduce a Monte-Carlo simulation and resampling approach.
Main Methods:
- Review of clinical and statistical challenges posed by multiple endpoints in clinical trials.
- Simulation studies to demonstrate Type 1 error inflation when multiplicity is ignored.
- Overview and discussion of existing global statistical methods for multiplicity adjustment.
- Introduction of a Monte-Carlo simulation and resampling approach for Type 1 error control.
Main Results:
- Ignoring multiplicity in clinical trials inflates the Type 1 error probability, increasing the risk of false positive treatment effect claims.
- Simulation results quantify the extent of Type 1 error inflation under various scenarios.
- Global methods and the proposed Monte-Carlo approach offer effective strategies for controlling Type 1 error.
Conclusions:
- Properly addressing multiplicity due to multiple endpoints is critical for the integrity of clinical trial results.
- The proposed Monte-Carlo simulation and resampling method provides a robust approach to control Type 1 error probability.
- Implementing these statistical strategies ensures the reliable identification of efficacious therapies.
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