1. (Full-day) Adaptive Designs

Instructors

Dr. Frank Bretz is a Distinguished Quantitative Research Scientist at Novartis. He has supported the methodological development in various areas of pharmaceutical statistics, including dose finding, multiple comparisons, and adaptive designs. Frank is an Adjunct Professor at the Hannover Medical School (Germany). He has authored or co-authored more than 150 articles in peer-reviewed journals and four books. Frank is a Fellow of the American Statistical Association.

Dr. Dong Xi is an Associate Director in the Statistical Methodology group at Novartis. He has supported development and implementation of innovative statistical methodology in multiple comparisons, dose finding, and group sequential designs, and has provided consulting service in various therapeutic areas. He is an associate editor of Statistics in Biopharmaceutical Research. He has authored or co-authored articles and book chapters on related topics.

Outline

Clinical trials play a critical role in pharmaceutical drug development. New trial designs often depend on historical data, which, however, may not be accurate for the current study due to changes in study populations, patient heterogeneity, or different medical facilities. As a result, the original plan and study design may need to be adjusted or even altered to accommodate new findings and unexpected interim results. Through carefully thought-out and planned adaptation, the right dose can be identified faster, patients can be treated more effectively, and treatment effects evaluated more efficiently.

This one-day short course will introduce different types of adaptive designs tailored for adaptive dose finding and confirmatory clinical trials. Practical considerations will be illustrated with case studies. Types of adaptive clinical trial designs covered in this course include adaptive dose finding studies using optimal designs to allocate new cohorts of patients based on the accumulated evidence, blinded and unblinded sample size re-estimation as well as adaptive designs for confirmatory trials with treatment or population selection at interim.

Prerequisites

A laptop and a LCD projector.

2. (Half-day morning) Analysis of Electronic Health Record Data

Instructor

Dr. Xu Shi is an Assistant Professor in the Department of Biostatistics at University of Michigan. She received her PhD in Biostatistics from University of Washington. Her research focuses on developing statistical methods for electronic health record (EHR) and claims data. She develops scalable and automated pipelines for curation and harmonization of EHR data across healthcare systems. She also develops causal inference methods that harness the full potential of EHR data to address comparative effectiveness and safety questions. She co-leads the Advanced Analytics Core of the FDA’s Sentinel Initiative Innovation Center to develop innovative statistical methods to monitor the safety of FDA-regulated medical products and explore novel ways to utilize information from distributed EHR data partners.

Outline

The growing availability of electronic health record (EHR) data is opening new opportunities for research. Ongoing efforts have been made to integrate large-scale EHR data across healthcare systems, as well as to link EHR data with biobank, insurance claims, registries, and death indices. With such cost-effective data sources, the health of an individual is now characterized with unprecedented precision and depth, facilitating contemporary research that makes the transition from data to knowledge. This course will offer an overview of the opportunities and challenges in using EHR data for research purposes. We will first introduce the basic structure of EHR data and the different types of errors and biases that lead to research challenges. We will then discuss statistical methods to tackle some of the challenges, including the development of phenotyping algorithms, methods for measured and unmeasured confounding adjustment, and automated quality control and harmonization of EHR data.

Prerequisites

Introductory statistics or practical experience in data science.

3. (Half-day afternoon) Use of External Evidence in the Design and Analysis of Clinical Trials

Instructor

Dr. Satrajit Roychoudhury is a Senior Director and a member of Statistical Research and Innovation group in Pfizer Inc. Prior to joining, he was a member of Statistical Methodology and consulting group in Novartis. He started his career as a research statistician in Schering Plough Research Institute (now Merck Co.). He has 15 years of extensive experience in working with different phases of clinical trials for drug and vaccine. His area of research includes survival analysis, use of model-based approaches and Bayesian methods in clinical trials. He has co-authored several publications in peer reviewed journals and book chapters. In addition, he has provided training and workshop in major statistical conferences.

Satrajit is a member of American Statistical Association (ASA) and Drug Information association (DIA). He served as the industry co-chair for ASA Biopharmaceutical Section Regulatory-Industry Workshop in 2018 and a member of current DIA Regulatory-Industry Statistics forum scientific committee. Satrajit was a recipient of a Young Statistical Scientist Award from the International Indian Statistical Association in 2019.

Outline

A Bayesian approach provides the formal framework to incorporate external information into the statistical analysis of a clinical trial. There is an intrinsic interest of leveraging all available information for an efficient design and analysis. This allows trials with smaller sample size or with unequal randomization. Examples include early phases drug development, occasionally in phase III trial, and special areas such as medical devices, orphan indications and extrapolation in pediatric studies. Recently, 21st Century Cure Act and PUDUFA VI encourage the use of relevant historical data for efficient design. An appropriate statistical method in this context needs to leverage “borrowing” of information while considering the heterogeneity between historical and current trial. In this short course, we'll cover the statistical frameworks to incorporate trial external evidence with real life example. This course will provide an overview of available approaches and introduce the meta-analytic predictive (MAP) framework for borrowing historical data. The MAP approach is based on Bayesian hierarchical model which combines the evidence from different sources. It provides a prediction for the current study based on the available information while accounting for inherent heterogeneity in the data. This approach can be used widely in different applications of clinical trial. The second part of the short course will focus on some applications of the MAP approach in clinical trial. These applications will be demonstrated using the R package RBesT, the R Bayesian evidence synthesis tools.

Prerequisites

The course will assume basic familiarity with Bayesian statistics, clinical trials and R.