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Updated: Jun 14, 2025

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

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Published on: June 21, 2018

Consistency-Based Approach to Adjust for Multiplicity in Confirmatory Subgroup Analysis.

Qiqi Deng¹, Qian Li², Naitee Ting²

¹Biostatistics, Moderna, Inc, Cambridge, MA, USA.

Statistics in Medicine

|June 11, 2025

Summary

This summary is machine-generated.

This study introduces a new statistical method to address multiplicity issues when testing personalized medicine treatments in both overall and biomarker-positive populations. The method ensures powerful and sensible hypothesis testing for biomarker-driven therapies.

Keywords:

biomarker enrichment multiplicity adjustment subgroup type I error inflation

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Area of Science:

Biostatistics
Clinical Trial Design
Personalized Medicine

Background:

Personalized medicine aims for targeted therapies, particularly in oncology, with expected higher efficacy in biomarker-positive patients.
Biomarker identification limitations and incomplete understanding of their role can lead to non-predictive markers.
Testing treatments on both overall and biomarker-positive subgroups is crucial but introduces multiplicity issues.

Purpose of the Study:

To develop a novel statistical approach for hypothesis testing in personalized medicine clinical trials.
To address type I error inflation arising from testing overall and biomarker-positive patient subgroups simultaneously.
To create a testing strategy that is both statistically powerful and logically sound for biomarker-driven treatments.

Main Methods:

Proposed a new statistical method that leverages the logical interconnections between hypothesis tests for overall and subgroup populations.
Incorporated adjustments for multiplicity by considering different rejection regions.
Focused on creating a sensible and powerful testing strategy for personalized medicine.

Main Results:

The proposed method effectively manages multiplicity issues inherent in testing treatments across overall and biomarker-positive patient groups.
The approach offers a balance between statistical power and logical coherence in hypothesis testing.
Demonstrates a sensible strategy for evaluating therapies where biomarker predictiveness may be uncertain.

Conclusions:

A novel statistical method is presented to appropriately adjust for multiplicity in personalized medicine trials.
The method provides a powerful and sensible approach to testing treatments in both general and specific patient subgroups.
This contributes to more reliable evaluation of targeted therapies, enhancing clinical trial design.