Biomarkers in Drug Development: Statistical Methods and Applications -

Description

Biomarker Statistical Considerations and Approaches for Clinical Trials addresses the central role biomarkers play in modern drug development. Across therapeutic areas and modalities, biomarkers inform disease classification, patient selection, trial design, and regulatory decision‑making. As precision medicine becomes standard practice, the ability to rigorously evaluate and interpret... Read more

Biomarker Statistical Considerations and Approaches for Clinical Trials addresses the central role biomarkers play in modern drug development. Across therapeutic areas and modalities, biomarkers inform disease classification, patient selection, trial design, and regulatory decision‑making. As precision medicine becomes standard practice, the ability to rigorously evaluate and interpret biomarker evidence has become essential. Yet effective biomarker use requires more than biological insight alone—it demands sound statistical methodology, principled study design, and careful consideration of emerging statistical approaches.

This book provides a comprehensive, end‑to‑end treatment of biomarker methodology from a quantitative perspective. Bringing together contributions from leading experts in biostatistics, machine learning, and clinical development, it offers an integrated view of biomarkers across the full lifecycle of drug development—from discovery and validation to modeling, confirmation, and interpretation. Rather than presenting isolated techniques, the volume emphasizes unifying principles that connect classical biostatistics with modern causal inference, high‑dimensional data analysis, and AI‑driven approaches, reflecting real‑world biomarker development and deployment.

Key Features:

Covers core biomarker types, including prognostic, predictive, and surrogate biomarkers, with clear distinctions between roles and evidentiary requirements
Addresses advanced topics such as treatment effect heterogeneity, companion diagnostics, generalizability and transportability, and biomarker‑driven trial designs
Includes modern methodologies for high‑dimensional omics data, machine learning, artificial intelligence, and digital twins
Tackles practical challenges such as multiplicity, missing and censored data, and statistical validation across development stages
Integrates regulatory and applied perspectives throughout, linking methodology to clinical and development decision‑making

Designed for statisticians, data scientists, and quantitative researchers in industry, academia, and regulatory agencies—as well as clinicians seeking a deeper understanding of the statistical foundations of biomarker‑based evidence—this book serves as both a practical reference and a conceptual guide. It provides a durable foundation for navigating today’s biomarker‑rich landscape and for engaging thoughtfully with innovations shaping the future of precision medicine.

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Editor(s)

Biography

Guillaume Desachy graduated from the French National School of Statistics (ENSAI, M.Sc.) in 2011 and has since gained experience across the entire drug‑development lifecycle, from pre‑clinical research to product launch. His career spans academia (University of California, San Francisco), biotechnology (Enterome, France), and the global pharmaceutical industry, with roles at Bristol Myers Squibb, Servier, AstraZeneca, and Pierre Fabre, in both France and Sweden.

His work is driven by the conviction that robust statistics is essential to bringing the right medicine to the right patient. His co‑leadership of the Biomarkers European Special Interest Group over several years, together with his publications in precision medicine, attests to his sustained engagement and commitment to this field.

In addition to his career in the pharmaceutical industry, Guillaume has served as an advisor to biotechnology companies and has been actively involved in professional and non‑profit organizations. These include the Biopharmacy and Health Group of the French Statistical Society, alumni associations, and initiatives promoting equal opportunity.

Dr. Gina D’Angelo earned her PhD in Biostatistics from the University of Pittsburgh, focusing on missing data methods, followed by a postdoctoral fellowship emphasizing genetics and neuroimaging. Her career spans academia to industry with roles at Hoffmann-La Roche, United HealthCare/Ingenix, University of Pittsburgh Cancer Institute Biostatistics Facility, Washington University Division of Biostatistics, MedImmune, and AstraZeneca. Across diverse therapeutic areas, her work spans discovery through late-phase development and small to high-dimensional data, with leadership as principal investigator and collaborator on multiple NIH grants and clinical trials.

Dr. D’Angelo is a Director in Oncology Statistical Innovation at AstraZeneca, bringing over 25 years of academic and industry experience in biomarker-related statistical methods, biomarker discovery, and dose optimization. She provides statistical leadership across early- to late-phase development, shaping strategy in dose optimization, precision medicine, biomarker development, including a program centered on a novel AI biomarker in oncology. She has designed and consulted on numerous studies, advanced methodology for prognostic and predictive biomarkers, and authored 50+ publications. As an active researcher and educator, she teaches internal and conference courses on biomarkers and precision medicine, reviews for multiple journals, and is co-editing and authoring books on biomarkers and clinical trial design.

Dr. D’Angelo leads multiple precision medicine initiatives across the industry. She is very active with external collaborations across ASA and IDSWG. As editor, she brings rigorous statistical perspective and practical translational insight to the design and evaluation of biomarker-enabled clinical research.

Biomarkers in Drug Development Statistical Methods and Applications

Description

Table of Contents

Editor(s)

Biography

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