2025 UCSF-Stanford CERSI Bayesian Thinking in Clinical Research Course

Date: 
Thursday, January 23, 2025 - 10:00 am to Thursday, April 10, 2025 - 11:30 am
Location: 
Virtual Event - Times Shown in Pacific Time

Overview

The UCSF-Stanford Center of Excellence in Regulatory Science and Innovation (CERSI) is pleased to announce the 2025 Bayesian Thinking in Clinical Research Course.

Why this course? There are a variety of 4-hour or one-day short courses that cover some Bayesian concepts or examples. There are also many in-depth statistical courses that are steeped in mathematics, computation, and inference. This course is designed to be in the sweet spot: A more in-depth course on Bayesian thinking with real-life examples and applications that do not involve mathematics. The UCSF-Stanford CERSI Bayesian Thinking in Clinical Research Course is meant to focus on concepts that will allow students to have engaging conversations with statisticians and review the clinical trial literature with a more educated perspective on inferring what is likely to be true.

Bayesian Statistics has been a major branch of statistical science for centuries but has had limited utility in practical applications for a wide variety of reasons. Bayesian methods are now emerging as a useful and powerful alternative to hypothesis testing and frequentist statistical approaches based on p-values. Bayesian methods offer more information and easier interpretation due to direct estimation of the probability that a conclusion is true given the data observed in a trial. Bayesian Statistical methods are based on incorporating prior knowledge into the analysis of newly generated experimental data to update our knowledge of a scientific hypothesis in a quantitative way. In this sense, the Bayesian approach is more aligned with scientific endeavors that continually build on previous knowledge by performing experiments and analyzing data to come to a better understanding of natural phenomena.

Participants will have the opportunity to learn Bayesian concepts and statistical principles for how to assess the likelihood of a hypothesis being true or false. The initial set of lectures will focus on broad principles of Bayesian thinking with subsequent lectures focused on more detailed implementation in clinical trials. Participants will be exposed to a broad range of case studies covering a variety of therapeutic areas and phases of drug development, including phase 3 trials for regulatory approval. The lectures will cover key Bayesian concepts and terminology to enable the audience to read and understand the publication on Bayesian trials in medical literature. All lectures will focus on principles and concepts without the underlying mathematics. Thus, the material should be accessible to a broad scientific and clinical audience and may also help statisticians who have not been exposed to Bayesian methods.

This is a virtual course comprised of twelve 90-minute sessions delivered live by experts in the field of Bayesian statistics and its applications to clinical trials. Sessions will be held on Thursdays, with some exceptions, from January 23, 2025, through April 10, 2025, from 10 – 11:30 am Pacific Time (1 – 2:30 pm Eastern Time). Each session may include pre-reading assignments, lectures, and discussion of case studies. Participants who successfully complete the course will be issued a Statement of Completion from the UCSF-Stanford Center of Excellence in Regulatory Science and Innovation (CERSI). Sessions will be recorded and available to all participants for the duration of the course.

Note: This course is intended for professional development and is not accredited for CME or PMP credit.

Learning objectives

  • Discuss how Bayesian methods are used in the design, analysis, and interpretation of clinical studies.
  • Explain factors that are important when considering the use of a Bayesian approach.
  • Explain the fundamental differences between Frequentist hypothesis testing and Bayesian inference (particularly the contrast between p-values and Bayesian posterior probabilities).
  • Interpret clinical literature that uses Bayesian methods for inference and interpretation.
  • Describe the basics of decision-making when using Bayesian inference (e.g., interim analysis, study success criteria, probability of study success, go/no-go decisions in drug development).
  • Explain the flexibility available for adaptive study designs including the inclusion of interim analyses.
  • Discuss the use of Bayesian methods to extrapolate efficacy or safety findings to another population (e.g., adults to pediatrics); to borrow information across subgroups to estimate more precise treatment effect in each subgroup.

Target audience

  • Early- to mid-career professionals involved in clinical trials (industry, academia, and government) who would like a broad overview of the latest developments in the application of Bayesian methods in clinical research.
  • Faculty members who are interested in using clinical trials to advance medical practice.
  • Trainees (students/residents/postdocs) who would like to complement their training and research in basic and applied statistics through the review of case studies and examples.

A basic understanding of statistical hypothesis testing and clinical trial design and execution is necessary. Familiarity with regulated clinical drug development would also be helpful but not necessary.

Registration

Register Here

The registration fees for this course are shown below.
Group discounts (for General Admission/Early Bird tiers only): $250 off per registrant for 3-6 registrants, $500 off per registrant for 7-9 registrants, and $750 off per registrant for 10 or more registrants from the same institution. For information on how to access group discounts, please contact [email protected].

Deadline to register: January 9, 2025 at 11:59 pm PT

Deadline for refunds: January 9, 2025

Information on course access will be sent to registrants in January.

Registration Category Registration Fee
General Admission $2,500
Early Bird (new deadline: Dec 8, 2024) $2,000
University/Government * $900
University/Government-Affiliated Trainees ** (students/residents/fellows/postdocs) $100

*To be eligible for the University/Government rate, you must be currently affiliated with a university or government institution and sign up using an email address ending in .edu/.gov/.mil.

**To be eligible for the University/Government-Affiliated Trainee rate, an individual must meet all the following criteria:

  1. Provide proof of CURRENT affiliation with a university or government trainee program. Acceptable documents include an enrollment verification certificate, class schedule for the current academic term, or an acceptance letter into the current trainee program.
  2. Use an email address ending in .edu, .gov, or .mil for registration (other university-affiliated trainee email addresses are accepted with proper documentation).
  3. Confirm that they are not a faculty member, do not hold a full-time university or government staff position, and are not eligible for full staff benefits which are typically reserved for permanent, full-time employees.

Please email proof of your trainee status and a declaration confirming your non-faculty and non-full staff eligibility to [email protected] upon registering.

Course Schedule

Date Topic(s) Instructors

PART 1 - OVERVIEW

SESSION 1:

January 23, 2025

Basics of Statistical Inference

Stephen J. Ruberg, PhD (President, Analytix Thinking; formerly Distinguished Research Fellow, Advanced Analytics, Eli Lilly and Company)

SESSION 2:

January 30, 2025

Type 1 Error and P (False Positive Finding) Stephen J. Ruberg, PhD (President, Analytix Thinking; formerly Distinguished Research Fellow, Advanced Analytics, Eli Lilly and Company)

SESSION 3:

February 6, 2025

Type 2 Error, Power and P (Study Success) Stephen J. Ruberg, PhD (President, Analytix Thinking; formerly Distinguished Research Fellow, Advanced Analytics, Eli Lilly and Company)
PART 2 - APPLYING BAYESIAN CONCEPTS

SESSION 4:

February 13, 2025

Development of prior and sequential learning. Impact and Pitfalls of the Choice of the Prior

Natalia (Natasha) Muehlemann, MD, MBA (VP, Clinical Development & Innovative Statistical Consulting - Cytel)
Jan Priel, PhD (Senior Research Consultant - Cytel)

SESSION 5:

February 20, 2025

Bayesian borrowing and modeling in trials

Natalia (Natasha) Muehlemann, MD, MBA (VP, Clinical Development & Innovative Statistical Consulting - Cytel)
Kyle Wathen, PhD (VP, Scientific Strategy & Innovation - Cytel)

PART 3 - CASE STUDIES (6)

SESSION 6:

February 27, 2025

A Case Study for Bayesian Design in Confirmatory Trials: BNT162b2 mRNA COVID-19 Vaccine Development

Satrajit Roychoudhury, PhD (Executive Director/Head of Statistical Research & Innovation, Pfizer Inc.)

SESSION 7:

March 6, 2025

Rebyota: The Design of a Phase 3 Trial with Statistical Borrowing from Phase 2

Zhong Gao, PhD (Statistical Reviewer, FDA)
Scott Berry, PhD (Senior Statistical Scientist, Berry Consultants)

SESSION 8:

March 13, 2025

Adaptive Designs and Interim Analysis

Ross Bray, PhD (Senior Advisor, Statistical Innovation Center - Diabetes, Eli Lilly and Company)

SESSION 9:

March 20, 2025

Bayesian Approaches to Pediatric Drug Development Using Extrapolation

Margaret (Meg) Gamalo, PhD (VP, Statistics Head for Inflammation and Immunology, Pfizer R&D)
Robert "Skip" Nelson, MD, PhD (Executive Director, Pediatric Drug Development, Child Health Innovation Leadership Department (CHILD), J&J Innovative Medicine)

SESSION 10:

March 27, 2025

Bayesian hierarchical model for subgroup analysis Yun Wang, PhD (Deputy Division Director for Division of Biometrics II in the Office of Biostatistics (OB) at Center for Drug Evaluation and Research (CDER), Food and Drug Administration (FDA))

SESSION 11:

April 3, 2025

From data to decisions, the power of Bayesian Inference in Safety

Pritibha Singh, Dr sc. (in-progress), MS, MBA (Analytics and Insights, Corporate Affairs, Novartis AG)
Melvin Munsaka, PhD (Senior Director and Head of Safety Statistics, Statistical Sciences, AbbVie Inc.)
Mani Y Lakshminarayanan, PhD (Global Product Statistician, Early Oncology Biometrics, AstraZeneca; Fellow, American Statistical Association)

 

OVERALL SUMMARY

SESSION 12:

April 10, 2025

The Epistemological Comparison of Bayesian Thinking versus Frequentist Thinking Stephen J. Ruberg, PhD (President, Analytix Thinking; formerly Distinguished Research Fellow, Advanced Analytics, Eli Lilly and Company)

Staff

Stephen J. Ruberg, PhD
Course Director

President, Analytix Thinking

Holly Ly, PharmD
Course Administrator

Education Coordinator, UCSF-Stanford CERSI

Please email [email protected] with any questions.