
Master Protocol Clinical Trials for Evidence Generation by Ruixiao Lu
Drug development is at a crossroads. As personalized medicine and targeted therapies expand, traditional clinical trial models have become too slow, costly, and inefficient. To meet the urgent need for faster, reliable evidence generation and decision making, researchers and regulators alike have turned to innovative trial designs. Among these, master protocols stand out as a transformative approach, allowing multiple therapies, disease subtypes, or objectives to be studied under a single, integrated framework. Proven successful in oncology, COVID-19, and beyond, they improve efficiency, reduce participant exposure to ineffective treatments, and accelerate meaningful outcomes with patient centric approach, prompting regulatory guidance and global adoption.
This book provides a multidisciplinary exploration of master protocol clinical trials, blending clinical, statistical, regulatory, operational, economic insights and patients perspectives. It examines both opportunities and challenges in design and implementation, and provides in-depth summary and discussion on real-world examples such as I-SPY, ROAR, and RECOVERY. A practical guide for researchers, statisticians, regulators, and industry professionals, it demonstrates how master protocols can reshape drug development and bring innovative therapies to patients faster.
● Provides comprehensive, theoretical and practical aspects for master protocol trials
● Includes most recent development in the master protocol clinical trials
● Covers broad topics such as trial designs, governance oversight, software implementation, database and data pipeline, multi-regional trials, risk and benefit with operational solutions, economic considerations, regulatory perspectives, patient engagement and diversity
● Deep dives into numerous real world case studies, their underlying thinking, challenges and success.
● Includes global perspective in multi-regional clinical development
-
R for Health Technology Assessment
-
Value of Information for Healthcare Decision-Making
-
Bayesian Models in Health Technology Assessment
-
Statistical Methods for Immunogenicity Assessment
-
Randomization, Masking, and Allocation Concealment
-
Exposure-Response Modeling
-
Mixture Modelling for Medical and Health Sciences
-
Data Analysis with Competing Risks and Intermediate States
-
Statistics In the Pharmaceutical Industry
-
Design and Analysis of Bioavailability and Bioequivalence Studies
-
Mixed Effects Models for the Population Approach
-
Multiple Testing Problems in Pharmaceutical Statistics
-
Machine Learning for Microbiome Statistics
-
Does This Treatment Cause That Outcome?
-
Comparative Effectiveness and Personalized Medicine Research Using Real-World Data
-
Age-Period-Cohort Models
-
Biostatistics for Bioassay
-
Statistical Methods for Survival Trial Design
-
Every Coin Has Two Sides
-
Group Sequential and Adaptive Methods for Clinical Trials
-
Empirical Likelihood Method in Survival Analysis
-
Multiregional Clinical Trials for Simultaneous Global New Drug Development
-
Adaptive Design Methods in Clinical Trials
-
Clinical Trial Data Analysis Using R and SAS
-
Bayesian Cost-Effectiveness Analysis of Medical Treatments
-
Statistical Methods for Drug Safety
-
Emerging Non-Clinical Biostatistics in Biopharmaceutical Development and Manufacturing
-
Design & Analysis of Clinical Trials for Economic Evaluation & Reimbursement
-
Meta-Analysis in Medicine and Health Policy
-
Model-Assisted Bayesian Designs for Dose Finding and Optimization
-
Statistical Methods in Drug Combination Studies
-
Design and Analysis of Clinical Trials for Predictive Medicine
-
Frailty Models in Survival Analysis
-
Bayesian Methods in Health Economics
-
Survival Analysis in Medicine and Genetics
-
Clinical Trial Methodology
-
Applied Surrogate Endpoint Evaluation Methods with SAS and R
-
Repeated Measures Design with Generalized Linear Mixed Models for Randomized Controlled Trials
-
Design and Analysis of Animal Studies in Pharmaceutical Development
Ruixiao Lu
Dr. Ruixiao Lu is a recognized leader in the biotech and biopharma industry, currently serving as Vice President, Head of Biostatistics & Statistical Programming at Alumis Inc. She has held senior leadership roles at various companies, where she led end-to-end data analytical teams supporting product development, medical affairs, and strategic initiatives. An elected Fellow of American Statistical Association (ASA), she is dedicated to advancing statistics and data science through professional organizations such as ASA, BBSW, and DahShu, as elected Board Director of ASA (2026- 2028), BBSW President and Board Member (2024- 2026), and co-founder of DahShu (2015- ), and is deeply engaged in cross-disciplinary collaborations, with a proven record of success to promote scientific excellence and improve public health outcomes, such as the 2023 FDA-AACR-ASA Oncology Overall Survival Workshop.
Jingjing Ye
Dr. Jingjing Ye is executive director and global head of Product Management and Data Insights (PMDI) in Global Statistics and Data Sciences (GSDS) at BeOne Medicines, leading a diverse global team in R&D for their data and analytical initiatives. With over 18 years of experience in the pharmaceutical industry and US FDA, she focuses on cancer drug discovery and development, covering the full spectrum of patients' treatment journeys. Her research interests include innovative trial designs for small populations. As a Fellow of the American Statistical Association, she is actively engaged in advancing statistics and data science in pharmaceutical drug development through professional organizations. She is also an elected ICSA board of directors (2026-2028).
Chengxing (Cindy) Lu
Dr. Chengxing (Cindy) Lu is a Senior Director of Oncology Biometrics in AstraZeneca, where she leads a team of statisticians dedicated to early-phase oncology development. Throughout her extensive career in drug discovery and development, her work has spanned the full spectrum of the field, encompassing early and late-stage clinical development as well as post-marketing, medical affairs, reimbursement and commercial strategies. Cindy is deeply committed to driving innovation and enhancing efficiency through multidisciplinary collaborations. She is actively engaged with the professional community through organizations such as the American Statistical Association (ASA), DahShu, DIA, and the International Society of Biopharmaceutical Statistics (ISBS), where she advocates for advanced statistical methodologies and promotes best practices that integrate multidisciplinary perspectives for real-world applications in drug development.
William (Bill) Wang
Dr. William (Bill) Wang is Executive Director in the Department of Biostatistics and Research Decision Sciences (BARDS) at Merck Research Laboratories (MRL). During 25+ years of experience in MRL, he has contributed extensively to clinical trial design, analysis/reporting, and regulatory filings across multiple therapeutic areas. Bill has led the design and build-up of MRL’s global biometrics operations in Asia-Pacific, and has spearheaded the establishment of the Clinical Safety Statistics group. Bill is an elected ASA fellow and has served as a core member in the ICH E17 expert working group and implementation working group.
| SKU | Unavailable |
| ISBN 13 | 9781032544526 |
| ISBN 10 | 103254452X |
| Title | Master Protocol Clinical Trials for Evidence Generation |
| Author | Ruixiao Lu |
| Series | Chapman And Hall Crc Biostatistics Series |
| Condition | Unavailable |
| Binding Type | Hardback |
| Publisher | Chapman and Hall/CRC |
| Year published | 2026-04-01 |
| Cover note | Book picture is for illustrative purposes only, actual binding, cover or edition may vary. |
| Note | Unavailable |






































