Adaptive Clinical Trials Conference & Workshop DVD
About the DVD:
Adaptive designs have the potential to accelerate clinical trials while cutting costs. An adaptive design means that the dosing, eligibility criteria, sample size, or treatment settings can be adjusted during the course of the trial as evidence accumulates. The final goal of the adaptive clinical trial is to bring technological advances to patients in the most efficient manner. The Adaptive Clinical Trials program is highlighting an array of topics, including adaptive designs within the context of a development program, Bayesian analysis in adaptive design, dose ranging studies, biomarker-driven trials, regulatory issues associated with adaptive design, and more.
About ADAPT Congress:
Old notions of personalized medicine are changing from nice to have, to necessary to have. The benefits to the patient, as well as the business opportunities a targeted therapy approach present are driving this change. The ADAPT 2011 Congress brings together senior executives and key strategic players from across Pharma, Diagnostic, Clinical and Informatics to work together to shape the future of personalized medicine towards a targeted approach. Case studies, discussion groups, short courses and technology highlights will explore the relationship between biomarkers and the promise of personalized therapy, how circulating tumor cells (CTCs) could better track the progression of cancer, and adaptive trials that bring targeted therapies to market.
About the DVD:
Over 249 Minutes
Site License: $1380
Conference At A Glance:
FDA Draft Guidance on Adaptive Designs: Overview, Key Messages, and Impact
José Pinheiro, Ph.D., Senior Director, Biostatistics, Johnson & Johnson Pharmaceutical Research and Development, LLC
The draft guidance on Adaptive Designs (AD) clarifies the FDA position on a wide range of topics related to the planning, execution and analysis of AD, being expected to have a major impact on the future utilization of these methods in clinical drug development. Pharmaceutical companies and the industry trade association, PhRMA, were actively engaged in reviewing the guidance to provide timely feedback to FDA. This presentation will provide an overview of the guidance, focusing on key regulatory concerns and recommendations on the use of AD in clinical trials, and discussing its potential impact on clinical development, as well as reactions from the pharma industry to its release.
Biography: José Pinheiro has a Ph.D. in Statistics from the University of Wisconsin–Madison, having worked at Bell Labs and Novartis Pharmaceuticals before his current position as Senior Director in the Quantitative Decision Strategies group at Johnson & Johnson PRD. He has been involved in methodological development in various areas of statistics and drug development, including dose-finding, adaptive designs, and mixed-effects models. A co-leader of the PhRMA working group on Adaptive Dose-Ranging Designs, he is also a co-developer of the NLME software in S-PLUS and R for linear and non-linear mixed-effects models.
The Continual Reassessment Method as a Prototype Adaptive Trial Design: Lessons and Implications
Steven Piantadosi, M.D., Ph.D., Phase One Foundation Chair and Director, Samuel Oschin Comprehensive Cancer Institute
This talk will discuss some of the history and underpinnings of the modified continual reassessment method (CRM) for dose-finding in oncology from the viewpoint of adaptive clinical trial design. The CRM is typical in some ways as a Bayesian design and atypical in others, making it a useful example to understand strengths and weaknesses of adaptive designs. The use of this design for dose-finding in oncology will be discussed as a starting point for reflecting on adaptive designs broadly. Most of the discussion will focus on the CRM itself and dose-finding versus dose-ranging, but some implications for later developmental designs will be highlighted. Practical implementation of the CRM will be illustrated using software developed by the author.
Biography: Dr. Piantadosi has been Director of the Cancer Institute at Cedars-Sinai Medical Center since 2008. Prior to that, he spent 20 years as Division Head in the Cancer Center at Johns Hopkins overseeing key aspects of its clinical research apparatus including Biostatistics, Bioinformatics, the Clinical Research Office, and Research Informatics. He also served on the Protocol Review and Monitoring Committee (serving as chair for several years), and the Institutional Review Board. He is a veteran of several cycles of Cancer Center Support Grant applications and awards at Johns Hopkins, as well as numerous Spore Grants and Program Projects. Dr. Piantadosi has also collaborated widely in NIH-funded multicenter investigations in roles ranging from principal investigator to a member of the steering committee. The common theme in much of this work is rigorous study design, appropriate methodology, and excellent technical execution of research design. Dr. Piantadosi is also an experienced teacher and mentor having taught in the classroom for 20 years where his textbook Clinical Trials: A Methodologic Perspective is a classic. He has also taught in applied venues such as the AACR/ASCO Vail Workshop, the NINDS Clinical Trial Workshop, and the AACR Sonoma Workshop for Cancer Biostatisticians. Since being at Cedars-Sinai, Dr. Piantadosi's leadership has focused on raising the volume and quality of peer-reviewed basic science and translational research. Particular emphasis has been placed on development of necessary research infrastructure, setting the Cancer Institute on a course for NCI designation.
Effective Design of Phase II and Phase III Trials: An Over-Arching Approach
Christopher Jennison, Ph.D., Professor of Statistics, University of Bath, UK
We consider the joint design of Phase II and Phase III trials following a decision theoretic approach with a gain function arising from a positive Phase III outcome and costs for sampling and time to a positive conclusion. With a prior for the dose response model and a risk curve for the probability that doses fail on safety grounds, the challenge is to optimize study designs for comparing doses in Phase II, choosing the dose or doses to progress to Phase III, and the Phase III design itself. We show it is feasible to tackle this problem and discuss generalizations from an initial, simple formulation.
Biography: Christopher Jennison is Professor of Statistics at the University of Bath, UK. He was awarded his Ph.D. from Cornell University for research into the sequential analysis of clinical trials and has continued to work in this area for the past 25 years. He has published extensively on group sequential methods and adaptive designs. His book with Professor Bruce Turnbull, Group Sequential Methods with Applications to Clinical Trials, is a standard text on this topic and is widely used by practicing statisticians.
Professor Jennison's research is informed by experience of clinical trial analysis at the Dana Farber Cancer Institute, Boston, and a broad range of consultancy with medical research institutes and pharmaceutical companies in Europe, America and Asia. He has made numerous presentations at international conferences, in which he sets out to describe novel statistical methodology and its application to the design and analysis of clinical trials.
A Bayesian Adaptive Design Case Study in a Phase III Cancer Trial
Jason Connor, Ph.D., Statistical Scientist, Berry Consultants
This example of a Bayesian adaptive design for a Phase III oncology trial incorporates longitudinal modeling of patient outcomes and predictive probabilities. The primary outcome is overall survival, and the final analysis is a standard log-rank test. Meanwhile a Bayesian machinery is used “behind the curtain” to select the optimal sample size based upon accruing information at predefined sample size selection analyses. At each interim analysis, the adaptive design considers differences in progression-free survival and uses this to predict future overall survival differences based upon an internal and constantly updated longitudinal model. The sample size algorithm is based on two predictive probabilities.
Biography: Jason Connor is uniquely trained as a biomedical engineer and a Bayesian biostatistician. He has worked for Berry Consultants designing Bayesian adaptive trials for medical device and pharmaceutical trials for six years. He has coauthored over 50 papers in clinical journals. He is also an Assistant Professor of Medical Education at the University of Central Florida and formerly an Associate Editor of The American Journal of Gastroenterology.
Randomization Challenges and Solutions in Adaptive Design Trials
Olga Kuznetsova, Ph.D., Director, Scientific Staff, Clinical Biostatistics, Merck Sharp & Dohme Corp.
Adaptive designs bring to the forefront methodological and implementation challenges in the randomization area. Advanced randomization techniques help efficiently manage limited or expensive drug supplies in multi-center adaptive design studies, deal with an inconvenient allocation ratio common to dose-ranging studies or multi-arm studies with sample-size re-estimation, and provide balance in baseline covariates in small interim analysis samples. In this session, we will review examples of challenging randomization issues that arise in adaptive design trials and go over the solutions to those. Critical points in implementation of the randomization techniques in adaptive design trials will be discussed.
Biography: Olga Kuznetsova is a Director of Scientific Staff at the Late Development Statistics department of Merck, Sharpe, and Dohme, Inc. She has a Ph.D. in probability theory and mathematical statistics and more than 15 years of experience in clinical trials. In the last decade, her research interests centered around randomization techniques in clinical trials, in particular, advanced techniques that serve special needs of adaptive design trials. Her collaboration with colleagues on randomization issues resulted in more than 20 presentations and publications and several internal guidance documents on randomization topics. Olga co-chairs the technical group at Merck dedicated to efficiency in randomization and drug distribution issues and consults the adaptive design effort at Merck on randomization issues.
Key Data Considerations for Successful Adaptive Trial Implementations
Walter Boyle, Advanced Analytics Strategist, Center for Health Analytics & Insights, SAS Institute
Adaptive trials are a critical component in accelerating the drug development process. While advanced analytics and agile operations are certainly critical factors in the successful implementation of adaptive approaches, these core capabilities cannot be successful without a strong data management strategy. Biopharmaceutical companies continue to aggregate research data as each clinical trial is completed, but that data is minimally used beyond the primary goals of proving the hypothesis for a single trial and, where necessary, supporting the submission of a marketing application. Only recently have companies begun to manage their legacy clinical trials data so it can be used to drive advanced scientific and business decisions, such as those identified through modeling and simulation.
Biography: Walter Boyle is currently a senior consultant and analytics strategist for the SAS Center for Health Analytics and Insights, a think-tank and incubator for strategy, technology and analytics focusing specifically on disruptive, novel and innovative approaches to clinical trials and research. Prior to joining SAS Mr. Boyle worked on enterprise data and analytics strategy at a large CRO, focusing on trial operations and optimization.