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Related Experiment Video

Updated: May 7, 2026

Preparation of Peripheral Blood Mononuclear Cell Pellets and Plasma from a Single Blood Draw at Clinical Trial Sites for Biomarker Analysis

Preparation of Peripheral Blood Mononuclear Cell Pellets and Plasma from a Single Blood Draw at Clinical Trial Sites for Biomarker Analysis

Published on: March 20, 2021

Sample size and threshold estimation for clinical trials with predictive biomarkers.

Howard M Mackey¹, Thomas Bengtsson

¹Genentech, South San Francisco, CA 94080, USA.

Contemporary Clinical Trials

|September 26, 2013

Summary

Personalized drug development requires joint investigation of treatments and biomarkers. This study provides methods to efficiently determine drug efficacy, biomarker utility for treatment personalization, and how to define personalization in clinical trials.

Keywords:

Cut-point Diagnostic Lung cancer Personalized medicine Predictive Prognostic

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Last Updated: May 7, 2026

Preparation of Peripheral Blood Mononuclear Cell Pellets and Plasma from a Single Blood Draw at Clinical Trial Sites for Biomarker Analysis

Preparation of Peripheral Blood Mononuclear Cell Pellets and Plasma from a Single Blood Draw at Clinical Trial Sites for Biomarker Analysis

Published on: March 20, 2021

Area of Science:

Biostatistics
Clinical Trial Design
Pharmacogenomics

Background:

Personalized drug development is increasing with new biomarker discoveries.
Joint investigation of treatments and biomarkers is crucial when clinical benefit is not unequivocally linked to biomarkers.
Contemporary cancer treatments highlight the need for robust trial designs.

Purpose of the Study:

To sequentially address drug efficacy, biomarker-driven treatment personalization, and personalization definition.
To develop statistical methods for analyzing time-to-event data in personalized medicine trials.
To enhance the efficiency of clinical trial designs for personalized therapies.

Main Methods:

Utilizing the Cox proportional hazards model for time-to-event data analysis.
Deriving an asymptotically exact covariance matrix for parameter estimation.
Developing sample size formulae and inference methods for continuous biomarkers.

Main Results:

Demonstrated that assessing efficacy and personalization may involve contrasts with smaller variance, not just interaction terms.
Provided an efficient inference approach for continuous biomarker thresholds.
Identified strategies for improved trial efficiency, particularly for binary biomarkers or when biomarkers do not affect the control arm.

Conclusions:

The proposed methods offer a framework for robustly evaluating personalized treatment strategies.
The derived covariance matrix aids in optimizing clinical trial design for personalized medicine.
This approach is motivated by and applicable to modern cancer treatment studies.