Graduate School of health and Medical Sciences

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Introduction to advanced Bayesian adaptive trials: design and analysis
Provider: Faculty of Health and Medical Sciences

Activity no.: 3349-26-00-00	There are 11 available seats
	Enrollment deadline: 05/11/2025

Date and time	03.12.2025, at: 09:00 - 04.12.2025, at: 15:00
Regular seats	15
Course fee	3,000.00 kr.
Lecturers	Anders Granholm
ECTS credits	1.20

Contact person	Susanne Kragskov Laupstad E-mail address: skl@sund.ku.dk

Enrolment Handling/Course Organiser	PhD administration SUND E-mail address: phdkursus@sund.ku.dk

Enrolment guidelines This is a generic course. This means that the course is reserved for PhD students at the Graduate School of Health and Medical Sciences at UCPH. Anyone can apply for the course, but if you are not a PhD student at the Graduate School, you will be placed on the waiting list until enrollment deadline. After the enrollment deadline, available seats will be allocated to the waiting list. The course is free of charge for PhD students at Danish universities (except Copenhagen Business School), and for PhD students at NorDoc member faculties. All other participants must pay the course fee. Learning objectives The course will provide students with an introduction to advanced Bayesian adaptive trials, makings students able to participate in the design and analysis of such trials. A student who has met the objectives of the course will be able to: • Understand the most important methodological considerations in advanced Bayesian adaptive trials using adaptive stopping, arm dropping, and response-adaptive randomisation • Evaluate and compare selected performance metrics applicable to advanced Bayesian adaptive trial designs using statistical simulation • Understand how to specify, conduct, and evaluate simple Bayesian regression analyses of clinical trial data • Understand how to calculate sample-average treatment effects using G-computation • Evaluate adaptation rules for adaptive stopping, arm dropping, and response-adaptive randomisation Content The aim of the course is to provide an introduction to advanced Bayesian adaptive trials, i.e., trials using adaptive stopping, adaptive arm dropping, and response-adaptive randomisation. The first day will focus on trial design and cover: • An introduction to advanced adaptive trials designs • An introduction to Bayesian statistical methods • Key methodological decisions for advanced adaptive trials • Trial design specification and evaluation of performance metrics using statistical simulation The second day will focus on analysis and cover: • Bayesian analyses, model specification including covariates and priors • Bayesian model fitting and evaluation using Markov chain Monte Carlo methods and appropriate model diagnostics • Calculation of average treatment effect and posterior probabilities using G-computation • Adaptive (interim) analysis and evaluation of stopping rules and updating of allocation profiles The course focuses mostly on the practical application of the introduced methods and less on the theoretical/mathematical underpinnings. The course will switch between lectures and hands-on computer exercises. Of note, although the course focuses on stand-alone Bayesian adaptive trials, all topics covered are also particularly useful in connection with adaptive platform trials. Participants The course is targeted towards participants working with the design or analysis of clinical trials. To be able to follow the course, participants are expected to: • Have basic knowledge on statistics/data science (e.g., at a level corresponding to the Basic Statistics for Health Researchers PhD-course or similar) • Basic knowledge of using the R statistical software package • Basic knowledge on clinical trials (design or interpretation) Participants are not expected to have previous experience with advanced adaptive trial designs or Bayesian statistical methods. Relevance to graduate programmes The course is relevant to PhD students from the following graduate programmes at the Graduate School of Health and Medical Sciences, UCPH: Public Health and Epidemiology Biostatistics and Bioinformatics All graduate programmes Language English or Danish Form Two days consisting of intertwined lectures and computer exercises using R. Participants must bring a laptop with the desired software installed. Course director Anders Granholm, MD PhD assistant professor, Section of Biostatistics and Rigshospitalet, anders.granholm@sund.ku.dk Teachers Aksel Karl Georg Jensen, MSc PhD assistant professor, Section of Biostatistics Anders Granholm, MD PhD assistant professor, Section of Biostatistics and Rigshospitalet Dates 3 and 4 December 2025, both days 9-15 Course location CSS Registration Please register before 5 November 2025 Expected frequency Once yearly. Seats to PhD students from other Danish universities will be allocated on a first-come, first-served basis and according to the applicable rules. Applications from other participants will be considered after the last day of enrolment. Note: All applicants are asked to submit invoice details in case of no-show, late cancellation or obligation to pay the course fee (typically non-PhD students). If you are a PhD student, your participation in the course must be in agreement with your principal supervisor.

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