Bias and Randomization 1980 – 2020 – 2060

February 11, 2021

Learn on history of bias and randomization in oncology clinical trials and how to think about what constitutes persuasive evidence of treatment efficacy and safety 

February 11, 2020 – 11am-12pm EST – 5pm-6pm EST



In 1980 Jay Herson published the paper “Patient Registration in a Cooperative Oncology Group” in the first volume of Controlled Clinical Trials. The fortieth anniversary affords an opportunity to reflect on how much has changed and how much remains the same. The history of randomization 1980-2020 reflects the changes in what the research and regulatory communities considered as persuasive evidence of treatment efficacy and safety. This talk will encourage the audience to think about what they consider persuasive evidence. We will begin our journey with the primitive oncology trials of 1980 before arriving at the highly regulated and complex trial design and analysis paradigm of today. The role of randomization is considered among the challenges of defining estimands, historical controls, real world data, Bayesian methods, pragmatic trials and COVID-19. We will conclude with imagining clinical trials in the year 2060 where machine learning using aggregate data may change our definition of persuasive evidence. We will raise the question of whether different communities (industry, academia, regulators, clinicians, patients) can co-exist each with their own definitions of persuasive evidence and how will randomization fit into these definitions.


Participants will learn the history of bias and randomization in oncology clinical trials and how to think about what constitutes persuasive evidence today and in the future.


JAY HERSON, Ph.D. Senior Associate, Biostatistics
Johns Hopkins Bloomberg School of Public Health Baltimore, Maryland, USA 

Jay Herson received his Ph.D. in Biostatistics from Johns Hopkins University in 1971. He has worked as a biostatistician on clinical trials at several medical centers including the University of Texas M.D. Anderson Cancer Center, Houston, Texas. In 1983 he formed Applied Logic Associates, a Houston-based CRO that provided data management, biostatistical and regulatory services on clinical trials for pharmaceutical, biotechnology and medical device firms mostly in oncology. However, firms working in internal medicine and ophthalmology benefited through ALA’s transfer of sophisticated statistical methods developed for in cancer research. At the time that ALA was sold to Westat, Inc. in 2001 there were 50 ALA employees.  Jay has developed methods for design and analysis of clinical trials with planned interim analyses and sample size re-estimation. In 1988 he organized and chaired the first data monitoring committee in the pharmaceutical industry. His book, Data and Safety Monitoring in Clinical Trials, Second Edition, Chapman and Hall, 2017 is considered the definitive guide to safety review in industry – sponsored clinical trials. He is currently working as a drug development consultant and as an adjunct faculty member in Biostatistics at the Johns Hopkins Bloomberg School of Public Health in Baltimore where he has taught in several graduate courses, is a regular attendee at journal clubs and seminars and serves as a career counselor and placement advisor to master’s students. Jay’s second career is that of a futurist. He has written articles and been an invited speaker on the future of health care, medical practice and local government.

MARC BUYSE, ScD, Chief Scientific Officer at the International Drug Development Institute (IDDI). 

    • He holds a ScD in biostatistics from the Harvard School of Public Health (Boston, MA).
    • He is the founder of the International Drug Development Institute (IDDI) and of CluePoints, two biostatistical service organizations based in the US and Europe.
    • He is interested in clinical trial design, meta-analysis, validation of biomarkers and surrogate endpoints, statistical methods in oncology, statistical detection of errors and fraud, statistical monitoring of clinical trials, and medical data sharing (


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