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Effective sample preparation is crucial for accurate and reliable laboratory analysis. During this process, two significant sources of error can arise: concentration bias from improper sample splitting and contamination caused by methods used to reduce particle size, such as grinding or homogenization. Identifying and minimizing these potential errors is crucial to ensuring the validity of the analysis.
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Sample Size for Enriched Biomarker Designs With Measurement Error for Time-to-Event Outcomes.

Siyuan Guo1, Susan Halabi1, Aiyi Liu2

  • 1Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina, USA.

Pharmaceutical Statistics
|July 25, 2025
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Summary
This summary is machine-generated.

This study addresses challenges in targeted clinical trials for personalized medicine. We present a new sample size formula to adjust for biomarker misclassification, improving trial power in enriched biomarker studies.

Keywords:
biomarker misclassificationenriched designprevalencesample size

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Area of Science:

  • Biomarker discovery and validation
  • Clinical trial design and methodology
  • Personalized medicine and targeted therapies

Background:

  • Personalized medicine aims to tailor treatments to patient subgroups.
  • Targeted clinical trial designs enrich trials with biomarker-positive patients.
  • Tissue specimen heterogeneity can lead to biomarker misclassification and reduced trial power.

Purpose of the Study:

  • To evaluate the adverse impact of biomarker misclassification on the power of targeted clinical trials.
  • To propose and validate a sample size formula that adjusts for misclassification in targeted designs.
  • To enhance the efficiency and reliability of biomarker-driven clinical trials.

Main Methods:

  • Developing a statistical approach to adjust sample size for biomarker misclassification.
  • Deriving a novel sample size formula for targeted clinical trial designs.
  • Applying the proposed formula to two Phase III clinical trials in renal and prostate cancer.

Main Results:

  • Biomarker heterogeneity significantly impacts the statistical power of enriched clinical trials.
  • The proposed sample size adjustment formula effectively corrects for misclassification errors.
  • The adjusted sample size ensures adequate power for detecting treatment effects in targeted trials.

Conclusions:

  • Accurate sample size calculation is crucial for biomarker-enriched clinical trials.
  • The developed method provides a robust approach to overcome challenges posed by biomarker heterogeneity.
  • This methodology can improve the design and interpretation of personalized medicine trials.