Short Course 2: Bayesian Designs of Clinical Trials Using Historical Data: From Theory to Practice

TBD, May 18, 2025

Stanford Campus, TBD

Instructor: Ming-Hui Chen, University of Connecticut

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.

Abstract

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.

Short Course 4: On RMST in survival analysis

TBD, May 18, 2025

Stanford Campus, TBD

Instructor: Lu Tian, Stanford University

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.

Abstract

Since Professor Sir David Cox proposed the hazard ratio (HR) and its inference procedure in 1972, the HR has been routinely used for quantifying the treatment effect on time to event endpoint. The Cox regression model has also been commonly used for the association and prediction analysis. While an ideal summary measure for the treatment effect should be model-free to avoid the model misspecification at the analysis, the HR is not such a measure. Indeed, there are very few model-free measures for time to event endpoint. The most popular one is either based on the median failure time or the event rate at a specific time point. Both measures are “local” and cannot catch the short- or long-term survival profile.

In the past few years, issues of utilizing HR have been extensively discussed. The validity of the HR estimate depends on a strong model assumption; that is, the ratio of two hazard functions is constant over time. When the proportion hazards assumption is not met, the HR estimate is difficult, if not impossible to interpret. In fact, the parameter for which the empirical HR estimates is not a simple weighted average of the HR over time and it generally depends on the censoring distributions. This is highly undesirable. Moreover, even when the proportional hazards assumption is plausible, the physical/clinical interpretation of a single ratio such as the HR estimate between two groups can not be easily translated into the clinical utility of a new treatment in the absence of a reference hazard level.

In this short course, I will illustrate issues of the HR and present an alternative, that is, the t-year mean survival time (restricted mean survival time, RMST). It is the mean event-free time up to a specific time point, say, t-year. The RMST was introduced in the statistical literature in 1949, but had not got much attention until recently. Here is the list of the topics we will discuss in this short course.

1. Issues of Hazard Ratio (HR)

2. Restricted mean survival time (RMST)

3. Power comparison between RMST-based test and HR-based tests

4. Empirical choice of a truncation time for RMST

5. Study design based on RMST

6. RMST for stratified analysis of survival data.

7. RMST to non-inferiority trials

8. RMST to quantify long-term survival benefit

9. RMST to assess duration of response

10. RMST in the presence of competing risks

11. RMST for recurrent event time data

12. Average hazard as an extension of RMST.

Short Course 5: Bayesian Statistics and Bayesian Models for Practical Dose Finding and Dose Optimization Oncology Clinical Trials

TBD, May 18, 2025

Stanford Campus, TBD

Instructor: Yuan Ji, University of Chicago and Ying Lu, Stanford University

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.

Abstract

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.