+ Dose Finding in Clinical Development (May 8, 2024: Full day)
+ Bayesian Designs of Clinical Trials Using Historical Data: From Theory to Practice (May 8, 2024: Full day)
+ AI for Clinical Trials: Emulation, Design, Matching, and Q&A Tools (May 8, 2024: Halfday - PM Session)
+ Miscellaneous topics on Randomized Clinical Trials: Surrogate Marker Evaluation and Covariate Adjustment Strategies (May 11, 2024: Full day)
+ Bayesian Statistics and Bayesian Models for Practical Dose Finding and Dose Optimization Oncology Clinical Trials (May 11, 2024: Full day)
+ Analysis of cancer omics data: the network perspective (May 11, 2024: Halfday - AM Session)
Naitee Ting is a Fellow of American Statistical Association (ASA). He is currently a Director in the Department of Biostatistics and Data Sciences at Boehringer-Ingelheim Pharmaceuticals Inc. (BI). He joined BI in September of 2009, and before joining BI, he was at Pfizer Inc. for 22 years (1987-2009). Naitee received his Ph.D. in 1987 from Colorado State University (major in Statistics). He has an M.S. degree from Mississippi State University (1979, Statistics) and a B.S. degree from College of Chinese Culture (1976, Forestry) at Taipei, Taiwan. Naitee published articles in Technometrics, Statistics in Medicine, Drug Information Journal, Journal of Statistical Planning and Inference, Journal of Biopharmaceutical Statistics, Biometrical Journal, Statistics and Probability Letters, and Journal of Statistical Computation and Simulation. His book “Dose Finding in Drug Development” was published in 2006 by Springer, and is considered as the leading reference in the field of dose response clinical trials. The book “Fundamental Concepts for New Clinical Trialists”, co-authored with Scott Evans, was published by CRC in 2015. Another book “Phase II Clinical Development of New Drugs”, co-authored with Chen, Ho, and Cappelleri was published in 2017 (Springer). Naitee is an adjunct professor of Columbia University, University of Connecticut, and Colorado State University. Naitee has been an active member of both the ASA and the International Chinese Statistical Association (ICSA).
Dr. Qiqi Deng is a Senior Director in Clinical Biostatistics at Moderna. She leads a group of biostatisticians for translational, emerging programs and public vaccine in the therapeutic area of infectious disease. Before joining Moderna, she worked in Boehringer Ingelheim for thirteen years, with seven years serving as leading statistician for multiple projects, across different clinical development phases and therapeutic areas. After that, she worked as a methodology expert for six years, with focus on statistical methodology innovation globally. Her main research area includes hypothesis testing and modeling in dose finding, adaptive design, and pragmatic considerations in applying innovative methodology into real clinical studies. She publishes on wide array of statistical topics in her main area and beyond, e.g. multiplicity adjustment, Bayesian statistics. Dr. Deng received her bachelor’s degree in Mathematics from Peking University in China, and obtained her Ph. D. in Statistics from University of Minnesota.
Dr. Lili Zhu is currently a Director at Moderna. She is a lead clinical biostatistician working in Oncology clinical trials. Before joining Moderna in 2021, Lili worked in the Clinical Biostatistics group of Bristol Myers Squibb (BMS) for around 10 years, also at Amgen and Biovail in Toronto. In most of her industry career as a clinical biostatistician, she works on multiple programs in different therapeutic areas across early and late phases of clinical trials focusing on the clinical development strategy and planning, trial designs, analyses, submission and approval. Dr. Zhu obtained her PhD in Statistics at Temple University while working at BMS, with doctoral dissertation research on a predictive time-to-event modeling approach with longitudinal measurements and missing data.
In the process of drug discovery and drug development, understanding the dose-response relationship is one of the most challenging tasks. It is also critical to identify the right range of doses in early stages of clinical development so that Phase III trials can be designed to confirm some doses within this dose range. Usually at the beginning of Phase II, there is not a lot of available information to help guiding the study design. At this stage, Phase II clinical studies are needed to establish proof of concept (PoC), to identify a set of potentially effective and safe doses, and to estimate dose-response relationships.
Challenges in designing these studies include: selection of the dose frequency and the dose range, choice of clinical endpoints or biomarkers, and use of control(s), among others. Consequences of bad dose finding study designs may lead to the delay of the entire clinical development program or the waste of R&D investment. Misleading results obtained from poor designs could cause a Phase III program to confirm a wrong set of doses, or to stop developing a potentially useful drug. Therefore, it is critical to consider an entire drug development plan, to make best use of all the available information, and to include all relevant experts in designing Phase II dose response clinical trials. This presentation discusses some of these considerations.
Who should attend? Who wants to gain knowledge in dose finding process in clinical development, including but not limited to statisticians, pharmacometricians, clinicians and clinical pharmacologists, etc.
Agenda: Part I: Proof of Concept in a Phase II Trial Combined with Dose Ranging (By Naitee Ting) Traditional Phase II clinical development of new drugs treating chronic diseases separates one study for proof of concept (PoC), and other studies for dose ranging purposes. With the availability of ordinal linear contrast test (OLCT) and MCPMod, most recent Phase II combines PoC and dose ranging into a single trial. Under this circumstance, the PoC step can be considered from various angles. Accordingly, sample size based on various scenarios can reflect different considerations. Given a fixed total sample size of 300, this presentation discusses simulation results of PoC with different number of dose groups, and various sample size allocation strategies.
Part II: Dose Finding in oncology (by Qiqi Deng and Lili Zhu) Historically, dose determination in oncology has followed divergent paths from other non-oncology therapeutic areas due to the unique characteristics and requirements in Oncology. However, with the emergence of new drug modalities and mechanism of drugs in oncology, such as immune therapies, radiopharmaceuticals, targeted therapies, cytostatic agents, and others, the dose-response relationship for efficacy and toxicity could be vastly varied compared to the cytotoxic chemotherapies. The doses below the MTD may demonstrate similar efficacy to the MTD with an improved tolerability profile, resembling what is commonly observed in non-oncology treatments. Hence alternate strategies for dose optimization are required for new modalities in Oncology drug development.The short course in afternoon will delve into the historical evolution of dose finding from non-oncology to oncology, highlighting examples and summarizing the underlying drivers of change. Subsequently, a practical framework and guidance are provided to illustrate how dose optimization can be incorporated into various stages of the development program.
Course materials: Slides, and "Dose Finding in Drug Development", published by Springer, Edited by Ting, N (2006).
Dr. Ming-Hui Chen is a Board of Trustees Distinguished Professor and Head of Department of Statistics at University of Connecticut (UConn). He obtained his PhD in Statistics from Purdue University in 1993. He was elected to Fellow of International Society for Bayesian Analysis in 2016, Fellow of Institute of Mathematical Statistics in 2007, and Fellow of American Statistical Association in 2005. He received the University of Connecticut AAUP Research Excellence Award in 2013, the UConn College of Liberal Arts and Sciences (CLAS) Excellence in Research Award in the Physical Sciences Division in 2013, the University of Connecticut Alumni Association's University Award for Faculty Excellence in Research and Creativity (Sciences) in 2014, the ICSA Distinguished Achievement Award in 2020, and the Distinguished Science Alumni Award from Purdue University in 2023. He has published 460+ peer-reviewed journal articles and five books including two advanced graduate-level books on Bayesian survival analysis and Monte Carlo methods in Bayesian computation. He has supervised 42 PhD students. He served as, President of ICSA (2013), Chair of the Eastern Asia Chapter of International Society for Bayesian Analysis (2018), President of New England Statistical Society (2018-2020), and the 2022 JSM Program Chair. Currently, he is Co Editor-in-Chief of Statistics and Its Interface, inaugurated Co Editor-in-Chief of New England Journal of Statistics in Data Science, and an Associate Editor for several other statistical journals.
Bayesian sample size determination (SSD) has a long history. The early work on Bayesian SSD can be traced back to 1990’s. Recently, several new methods on Bayesian designs of clinical trials has been developed with a focus on controlling type I error and power. In this short course, an overview of the literature on Bayesian SSD will be provided. The general theory and various methods of Bayesian SSD will be presented. Informative prior elicitations to leverage external or historical data will be discussed in details. The recent development of software to implement various Bayesian SSD approaches will also be reviewed.
This short course starts with a brief review of early development of Bayesian SSD. Then, a comprehensive review of Bayesian methods for borrowing historical information and proper use of these methods in Bayesian clinical trial designs will follow. The short course will also cover the computational algorithms and recent available software on Bayesian SSD. The short course will also highlight several important applications in designing clinical trials to demonstrate the superiority of Bayesian SSD.
Ruishan Liu is an Assistant Professor of Computer Science at USC. She received her PhD in Electrical Engineering at Stanford University in 2022 and was a Postdoctoral Fellow in Biomedical Data Science at Stanford University from 2022 to 2023. Her research lies in the intersection of machine learning and applications in human diseases, health and genomics. She was selected as the Rising Star in Data Science by University of Chicago, the Next Generation in Biomedicine by Broad Institute, and the Rising Star in Engineering in Health by Johns Hopkins University and Columbia University. She led the project Trial Pathfinder, which was selected as Top Ten Clinical Research Achievement in 2022 and Finalist for Global Pharma Award in 2021.
Lu Tian is Professor of the Department of Biomedical Data Science at Stanford University. He received his Doctor of Science degree in Biostatistics from Harvard University. Dr. Tian has rich experience in statistical methodological research, and study design for randomized clinical trials. He has published more than 300 research papers in both statistical and clinical journals. He is an elected Fellow of American Statistical Association. His current research interest is in developing statistical methods in precision medicine, meta-analysis, randomized clinical trial, and survival analysis.
The course is divided into two areas. The first half focuses on the fundamental concepts of surrogate markers and their role within a robust statistical framework. This section covers the evaluation of surrogate marker strength and its application in enhancing the execution of randomized clinical trials. The instructional approach includes both theoretical discussions employing causal inference language for surrogate markers and practical demonstrations using real-world examples. The latter half of the course focuses on exploring methods for adjusting covariates to estimate the average treatment effect in randomized clinical trials. It establishes the validity and potential benefits of various covariate adjustment techniques, emphasizing improved precision and reduced bias. The session also introduces optimal covariate adjustment strategies for different types of endpoints, encompassing ordinal, survival, and longitudinal outcomes. Additionally, discussions encompass leveraging historical control data to inform and optimize covariate adjustment strategies. The course employs simulated studies and real data examples to vividly illustrate the impact and effectiveness of covariate adjustments.
Dr. Yuan Ji is Professor of Biostatistics at The University of Chicago. His research focuses on innovative Bayesian statistical methods for translational cancer research. Dr. Ji is author a large number of publications in peer-reviewed journals including across medical and statistical journals. He is the inventor of many innovative Bayesian adaptive designs such as the mTPI and i3+3 designs, which have been widely applied in dose-finding clinical trials worldwide. His work on cancer genomics has been reported by a large number of media outlets in 2015. He received Mitchell Prize in 2015 by the International Society for Bayesian Analysis. He is an elected fellow of the American Statistical Association.
Ying Lu, Ph.D., is Professor in the Department of Biomedical Data Science, and by courtesy in the Department of Radiology and Departement of Health Research and Policy, Stanford University. He is the Co-Director of the Stanford Center for Innovative Study Design and the Biostatistics Core of the Stanford Cancer Institute. Before his current position, he was the director of VA Cooperative Studies Program Palo Alto Coordinating Center (2009-2016) and a Professor of Biostatistics and Radiology at the University of California, San Francisco (1994-2009). His research areas are biostatistics methodology and applications in clinical trials, statistical evaluation of medical diagnostic tests, and medical decision making. He serves as the biostatistical associate Editor for JCO Precision Oncology and co-editor of the Cancer Research Section of the New England Journal of Statistics and Data Science. Dr. Lu is an elected fellow of the American Association for the Advancement of Science and the American Statistical Association. Dr. Lu initiated the Stat4Onc Annual Symposium with Dr. Ji and Dr. Kummar in 2017 and is the PI of the R13 NCI grant for this conference.
Project Optimus from FDA aims to shift the paradigm of oncology dose selection by emphasizing the importance of finding an optimal dose with desirable efficacy and safety. Traditionally, the optimal dose in oncology is equivalent to the maximum tolerated dose (MTD) since most oncology drugs have been cytotoxic, making the highest tolerable dose also the most efficacious dose. Due to the development of targeted and immune oncology therapeutics, MTD is no longer the default optimal dose. Statistical designs solely aimed at finding the MTD therefore cannot fulfill the requirement of Project Optimus. In this short course, I will provide a comprehensive review and introduction of practical statistical designs for dose optimization. Through the learning provided by this short course, attendees will be exposed to the main ideas and motivations for key innovative statistical designs and strategies. The short course is constructed not long to teach “hows” but “whys”, so that attendees will develop ability to assess the pros and cons of different designs for their individual needs in practice. The following topics will be covered with three sessions:
Session 1: Brief review of Bayesian statistics and modeling. This session will expose attendees to some basic concept and techniques of Bayesian statistics as many designs for early-phase oncology trials are Bayesian.
Session 2: Review of key dose-finding Designs, including but not restricted to 3+3, CRM, mTPI, mTPI-2 (keyboard), BOIN, and i3+3. This session will review a set of major dose-finding designs aiming to identify the MTD, which sets the stage for innovative dose-optimization strategies and designs.
Session 3: Introduce strategies and designs for oncology dose optimization. This session will introduce a few key designs and strategies for dose optimization like eff-tox dose-finding designs, expansion cohorts trials, backfill designs, seamless phase 1-2 designs, PK-empowered dose finding, and randomized dose comparison, etc.
Session 4: Q&A Throughout the short course, available software and tools will be illustrated for the course attendees. The short course is expected to run for 4 hours with two breaks.
Shuangge (Steven) Ma is a Professor of Biostatistics at the Yale School of Public Health. He obtained his Ph.D. in Statistics from the University of Wisconsin, Madison, and had his postdoc training at the University of Washington, Seattle. He has been conducting research in cancer biostatistics, genetic epidemiology, high-dimensional statistics, and survival analysis. More recently, he has been engaged in deep neural network research for low- and high-dimensional data.
High-throughput sequencing data has been extensively generated and analyzed in cancer research. In this short course, the “standard” individual-gene- and gene-set-based analysis will be first reviewed. In the past decade, there has been a surge in network-based analysis of cancer omics data. Such analysis takes a system perspective, may include its simpler counterparts as special cases, and can be more informative. In this course, the basic concepts of network analysis will be introduced, followed by different ways of (in particular, unconditional and conditional) network constructions. Analysis of key network properties, such as hubs and modules, will be introduced. Then, analysis (for example, regression) that takes network information into account will be described. A few miscellaneous topics, such as multiomics-based network analysis, will also be covered. Examples from recent studies will be presented along with the methods.