PSI Webinar: Application of Bayesian Methods in Confirmatory Trials

Date: Tuesday 3rd October 2023
Time: 14:00-15:30 BST
Speakers: Satrajit Roychoudhury (Pfizer), Kenneth Koury (Pfizer), Sebastian Weber (Novartis) and Emma Clark (Roche).

Who is this event intended for? Statisticians in the pharmaceutical industry with an interest in Bayesian methodology.
What is the benefit of attending? Attendees will have the opportunity to learn about Bayesian methods and their application in later stage studies.


This webinar is free of charge to both Members and Non-Members of PSI.


To register for this event, please click here.
Please note:  Due to unprecedented demand, access to the live session will operate on a first come, first serve basis - registration does not guarantee attendance for our most popular free events. If you are not able to gain entry to attend the webinar live, there will be a recording made available a week or two after the event. We thank you for your understanding, and for your interest in this topic!


The use of Bayesian methods within clinical development has greatly increased in recent years. This is particularly true for earlier stage trials but they are also being used in later stage work. In this webinar, three different examples of where Bayesian methodology has been used within confirmatory settings will be provided. The examples describe:

  • A Bayesian framework used to incorporate several interim analyses to monitor the trial for efficacy and futility, while controlling the overall type 1 error.
  • Use of the meta-analytic-predictive prior methodology, interpreting study data in the context of trial external data while accounting for between-trial heterogeneity.
  • Incorporating a hybrid external control arm using Bayesian dynamic borrowing with propensity score matching.

Speaker details



Title & Abstract

Satrajit Roychoudhury (Pfizer Inc.)

Kenneth Koury
(Pfizer Inc.)


Dr. Satrajit Roychoudhury is an Executive Director and a member of Statistical Research and Innovation group in Pfizer Inc. He has 17 years of extensive experience in working with different phases of clinical trials for drug and vaccine. His research interest includes survival analysis, use of model-based approaches and Bayesian methods in clinical trials. He served as the industry co-chair for ASA Biopharmaceutical Section Regulatory-Industry Workshop in 2018 and co-chair for DIA/FDA Biostatistics Industry and Regulator Forum in 2023. Satrajit is an elected Fellow of the American Statistical Association and recipient of Royal Statistical Society (RSS)/Statisticians in the Pharmaceutical Industry (PSI) Statistical Excellence in the Pharmaceutical Industry Award in 2023 and Young Statistical Scientist Award from the International Indian Statistical Association in 2019.

Ken Koury is Vice President and Head of Statistics and Modeling for Vaccine Clinical Research and Development at Pfizer.  He is responsible for all statistical aspects of global clinical research and development of vaccines to meet licensure and post licensure requirements, and he has over 35 years of experience in drug and vaccine development.  Ken has previously served as Co-Chair of the FDA/Industry/Academia Safety Graphics Working Group, on PhRMA’s Biostatistics and Data Management Technical Group, as Program Chair and Chair of the ASA Biopharmaceutical Section, and on the Steering Committee for several Regulatory-Industry Workshops.



Evolution of the COVID-19 vaccine during pandemic: transforming development paradigm

Vaccines are complex biological products which are administered to healthy individuals. Safety is therefore paramount; vaccine development often entails large, time-consuming, and resource-intensive studies to detect rare safety issues and to establish vaccine efficacy. Before a vaccine is licensed and brought to the market, it undergoes a long and rigorous process of research, followed by many years of clinical testing. However, such framework requires modification for COVID-19 vaccine development due to high public health demand. First half of this talk will present the operationally seamless development paradigm used to develop a mRNA vaccine for COVID-19. A Bayesian framework was used to incorporate several interim analyses to monitor the trial for efficacy and futility, while controlling the overall type 1 error. The Bayesian framework enabled us to obtain efficient designs using decision criteria based on a fully probabilistic framework. We’ll focus on the key statistical aspects and regulatory challenges of the design. The second part of the talk will highlight the important post approval steps including determination of booster dose and pediatric development. Publicly disclosed results will be summarized and discussed, as well as key interactions with regulatory authorities and scientific community.

Sebastian Weber

Sebastian Weber is working as Director in the Department of Advanced Methodology and Data Science at Novartis. He has worked extensivley on enabling the use of historical (control) information in clinical trials through consulting and working on tools to facilitate the application of historical control information from trial design to analysis. Furthermore, Sebastian has experience in designing Oncology phase I dose-escalation trails and is also involved in pediatric drug development programs, where he applies extrapolation concepts. His research interests include the application of pharmacometrics in statistics, model-based drug development and application of Bayesian methods for drug development.


Innovative Pediatric Development for Secukinumab in Psoriasis: Faster Patient Access, Reduction of Patients on Control

The pediatric development program of secukinumab in psoriasis was initially designed in a conservative manner based on limited knowledge of the drug. This had been the consequence of being formulated at an early stage of the adult program like it is mandated by law in major regions worldwide. Over time more knowledge on secukinumab accrued and novel innovative approaches as efficacy extrapolation became available as tools for drug development. This provided the opportunity to increase the efficiency of the already running pediatric development program of secukinumab substantially. At the time of the program amendment a study in severe psoriasis had been running and it was planned to be followed by a study in moderate psoriasis. As program amendment two changes were implemented: (i) efficacy extrapolation was introduced by way of a pre-specified predictive check using the data of the placebo-controlled severe study and (ii) the follow-up study in moderate was changed from a placebo-controlled study to an open-label study controlled by historical control data only. The historical control was pre-planned to also include the not yet observed placebo data of the severe study. These two analyses both relied on the meta-analytic-predictive prior methodology allowing to interpret study data in the context of trial external data while accounting for between-trial heterogeneity. This amendment led to the removal of the placebo arm from the moderate trial, a substantial reduction of the overall sample size in this program, and a significantly faster approval of secukinumab for the pediatric psoriasis population.

Emma Clark

Emma Clark has 30 years experience as a statistician in the UK Pharmaceutical Industry. Her early experience was for a UK Affiliate, supporting late phase studies across a number of therapeutic areas. Emma joined Roche Products Ltd 14 years ago where she has worked solely on oncology clinical trials in breast cancer and haematology. In 2020/2021, Emma participated in Company collaborations with the FDA on the use of a hybrid external control arm using Bayesian dynamic borrowing in a randomized phase 3 study in Diffuse Large B-Cell Lymphoma.

Bayesian dynamic borrowing of external data for overall survival: Experience of the FDA CID Pilot Program

This talk will focus on our experience of collaborating with the FDA on the design of a phase 3 randomised study in 1L Diffuse Large B-Cell Lymphoma (DLBCL) through the FDA's Complex Innovative Trial Designs (CID) Meeting Program. The study design incorporated a hybrid external control arm using Bayesian dynamic borrowing with propensity score matching for the analysis of overall survival, a key secondary endpoint with label-enabling potential.


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