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The population-wise error rate for clinical trials with overlapping populations
Werner Brannath1, Charlie Hillner1, Kornelius Rohmeyer2
1University of Bremen, Institute for Statistics and Competence Center for Clinical Trials, Bremen, Germany.
Statistical Methods in Medical Research
|December 1, 2022
Summary
This study introduces a population-wise error rate for clinical trials with multiple, overlapping patient groups. This new criterion helps manage type I errors, ensuring treatments are relevant to specific patient sub-populations in precision medicine.
Area of Science:
- Biostatistics
- Clinical Trial Design
- Precision Medicine
Background:
- Clinical trials increasingly target specific patient sub-populations using biomarkers.
- Existing methods for multiple testing may not be optimal for trials with overlapping populations.
- Precision medicine necessitates novel statistical approaches for evaluating targeted therapies.
Purpose of the Study:
- To introduce a new multiple type I error criterion for clinical trials involving multiple, overlapping populations.
- To define and exemplify the population-wise error rate (PWER) for assessing treatment efficacy in targeted populations.
- To provide methods for controlling PWER and constructing simultaneous confidence intervals.
Main Methods:
- Developed a new statistical criterion for controlling type I errors in multi-population trials.
- Defined the population-wise error rate as the probability of exposing a patient to an ineffective treatment.
- Proposed methods for controlling PWER via adjusted critical boundaries or p-values.
Main Results:
- The proposed population-wise error rate is relevant for intersecting sub-populations.
- Methods for controlling PWER and constructing confidence intervals were demonstrated.
- Illustrations showed potential power gains compared to family-wise error rate control.
Conclusions:
- The population-wise error rate offers a more relevant approach for clinical trials with overlapping sub-populations.
- This criterion is particularly useful in precision medicine to avoid exposing patients to ineffective treatments.
- The proposed methods can enhance statistical power in targeted therapy evaluations.
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