This year we are delighted to offer two pre-conference courses, set to run on Sunday 8 June from 1pm -  5pm. They will be held at the Novotel London Wembley. 

Pre-conference Course 1

Unlocking Insights: Advanced Pooled Analyses Techniques for Clinical Trial Statisticians

Course description:

This course provides a comprehensive introduction to pooled analyses of randomized controlled trial (RCT) data, with a focus on methodologies and applications essential for clinical trial statisticians. Pooled analyses provide significant benefits during various stages of drug development, and may help to examine subgroup effects, analyse rare (e.g., adverse) events, and estimate more individualized treatment effects. We will cover statistical techniques for analysing individual participant data (IPD) from multiple trials, with a particular focus on meta-analysis methods that address potential heterogeneity between study populations. To ground these concepts, the course will include applied case studies that demonstrate how IPD meta-analyses enhance the precision and applicability of findings, ultimately supporting more personalized and impactful analyses in clinical research. This course equips statisticians with the expertise to apply advanced meta-analysis techniques to real-world clinical trial data, strengthening their ability to conduct rigorous and meaningful analyses that inform evidence-based decision-making.

Organisers:

• Dr. Thomas Debray, Smart Data Analytics and Statistics B.V., The Netherlands

Thomas Debray, PhD is a distinguished statistician and academic who transitioned from academia into specialized consulting for the pharmaceutical industry, leveraging his expertise in real-world evidence (RWE), precision medicine, and evidence synthesis to drive innovation in clinical research. Thomas is the author of various methodological guidelines, including TRIPOD-Cluster, which enhances reporting standards for prediction models developed in pooled analyses and datasets with clustering. He also contributed to the latest ENCePP Methodological Guide, focusing on statistical methods to generate evidence for comparative effectiveness research from randomised clinical trials (RCTs), observational data, synthesis of published RCTs and cross-design synthesis.
As the founder of Smart Data Analysis and Statistics (SDAS), Thomas leads a team dedicated to advancing clinical trial methodologies and providing tailored statistical support to pharmaceutical companies and biotech firms. Under his leadership, SDAS has developed various analytical tools, including SimTOST, a comprehensive R package designed to optimize the design of bioequivalence trials with multiple treatment arms and (co-)primary endpoints. SDAS is also a trusted partner in pioneering academic research projects, such as the Marie Curie SHARE-CTD project, where it trains early-career researchers in advanced methodologies for analysing data across multiple clinical trials.

• Prof. Tim Friede, University Medical Center Göttingen, Germany

Tim Friede has served as Professor of Biostatistics at the University Medical Center Göttingen since January 2010, where he leads the Department of Medical Statistics. He holds a degree in Mathematics (Dipl.-Math.) from the University of Karlsruhe and a Ph.D. (Dr.sc.hum.) from the Faculty of Medicine at the University of Heidelberg.
In 2001, he began his career in academia as an NHS Training Fellow in Medical Statistics in the Department of Mathematics and Statistics at Lancaster University, where he was later promoted to Lecturer in Biostatistics. In 2004, he joined Novartis Pharma AG in Basel before transitioning to Warwick Medical School as an Associate Professor of Medical Statistics in October 2006.
In 2014, he earned the “Biometry in Medicine” certificate from the German Society for Medical Informatics, Biometry, and Epidemiology (GMDS) and the German Region of the International Biometric Society (IBS-DR), solidifying his credentials as a trial statistician.
Tim has been a member of the Sino-German Institute for Social Computing (SGISC) since 2015 and joined the SGISC Board of Directors in early 2020. Additionally, he became a member of the Campus Institute Data Science (CIDAS) in 2023.

Pre-conference Course 2

Adaptive and Complex Innovative Designs across trial phases for accelerated approval

Course description: 

Drug development is a time-consuming and expensive process, with the pharmaceutical industry facing significant hurdles in the development of novel therapies. Moreover, the attrition rate in clinical research has also been on the rise. As a result, the Food and Drug Administration (FDA) released the critical path initiative in 2004 and critical path opportunity list in 2006 thus highlighting the need of advancing innovative clinical trial designs. One of the innovations suggested was adaptively designed clinical trials, which allow for pre-specified modifications to one or more aspects of the design based on accumulating data from subjects in the trial. With the advent of novel therapies, regulatory bodies are focused on efficient implementation of adaptive trial designs to accurately achieve trial objectives. Adaptive designs are now widely used across all phases of clinical trials. Adaptive clinical trials may involve multiple experimental objectives, or aim to accelerate the time to approval for the product. In such scenarios, traditional (fixed sampling) designs can fail to meet the objectives of the sponsors and innovative approaches are needed. Recognizing the importance of the topic, the FDA has issued draft guidance on adaptive designs (FDA 2019). This guidance provides useful insights on scientific and operational aspects of these designs and highlights the importance of continued discussions among the FDA, pharmaceutical industry, academia, patients and the public on the topic. At the same time, the FDA has released guidance on Complex Innovative Design (CID) for drug or biological products (FDA 2020), which includes adaptive, Bayesian, and other novel clinical trial designs.

This course aims to provide an introduction to the use of adaptive designs across all phases of clinical research, highlighting its evolution, use and how it fits into the various regulatory initiatives such as Project Optimus and the CID programme, with a focus on statistical considerations. These designs are often more efficient, informative and ethical than traditional study designs, but pose specific challenges (both statistical and practical). The course will start with introducing the basics of different types of adaptive design methods and also the concept of Bayesian statistics which is frequently used for many of such designs. We would proceed to discuss the evolution of these designs and what makes them an attractive alternative to traditional clinical trial designs. We would then introduce the CID programme and Project Optimus, and discuss how such designs fit into the benefits of such regulatory initiatives and the challenges one can face when practically implementing such designs. During the last part of the course, we will focus on the methods of response adaptive randomization (RAR) and covariate-adjusted response adaptive (CARA) randomization, which has been widely discussed in the literature and also among the regulatory and industry, who have been weighing their usefulness against the operational challenges for their practical use. Following Roberson et al. (2023), we would also discuss the myths and practical challenges of using RAR and CARA methods, and explore how such methods can fit within the CID programme of FDA at various phases of clinical research.

The course would also involve interactive discussions of case studies of real-life clinical trials, with a focus on the statistical and practical considerations around the chosen study design. Our aim is to give a minimally theoretical overview of a wide range of innovative adaptations available for trial designs, with a focus on how these methods are being implemented in real-life trials and how such methods fit into the regulatory initiatives. The course would also discuss the goals, advantages and limitations of these adaptive methods. For each concept, we will describe the study design, the practical benefits for which this was selected, the statistical considerations that are required for a robust interpretation of results around hypothesis testing and estimation, and discussions of funding, preserving integrity and reporting results at study completion. We would present and share a 'Start Here' beginners' guide for adaptive designs, which collates a range of practical resources including papers and tutorials to simplify the use of adaptive designs in practice.

Organisers:

• Dr. Thomas Burnett, Lecturer, Department of Mathematical Sciences and Institute for Mathematical Innovation (IMI) University of Bath
• Dr. Ayon Mukherjee, C.Stat, Director Biostatistics, Eli Lilly 
• Dr. David Robertson, Senior Research Associate at the MRC Biostatistics Unit, University of Cambridge
• Dr Dr Sofia Villar, MRC Investigator (Programme Leader) at the MRC Biostatistics Unit, University of Cambridge

Course Fees

The registration fees for the pre-conference courses are as follows:

The number of places on these courses is limited, register today! You can register here: 

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