DIA 2025 Global Annual Meeting

Component Type: Tutorial
CE: ACPE 2.50 Application UAN: 0286-0000-25-506-L04-P; CME 2.50; IACET 2.50; RN 2.50

Pre-registration required and is an additional fee. *Please note: Short Courses are stand-alone events. Registration for the main conference, forum, etc. are not mandatory. Already registered? Log in to DIA's My Account My Events. This Short Course will be offered virtually – Join from anywhere! Statistical methods are a powerful tool used in clinical trials to assess whether the data support evidence of a treatment effect. It is therefore key that the results of any statistical analysis are interpreted correctly. However, understanding and interpreting statistical results can be challenging for non-statisticians. In this short course, participants will be introduced to common statistical methods applied to specific outcomes and their results. Some examples of potential statistical concepts to be covered include commonly seen hypothesis test, survival analyses, regression modelling, mixed models for repeated measures (MMRM), and adjusting for multiplicity. Important concepts such as p-values will be explained fully. Idealized examples as well as examples from publicly posted FDA reviews and results on clinicaltrials.gov will be used. Group discussions using example outputs (including examples from FDA reviews) will give participants the opportunity to apply their learning by critically evaluating statistical analysis approaches and interpreting statistical results, thus giving participants the tools necessary to effectively communicate with their colleagues involved in drug development. Receive $50 off your Global Annual Meeting total purchase by registering for two or more Short Courses. Purchases must be made at the same time to receive the discount. Discounts will be reflected on the last page of the cart. Registered attendees for this virtual Short Course will receive access to the course recording for 2 full months post-course! This allows you to remain flexible with your schedule and not worry if you need to step out momentarily. Have a conflict with the dates of the course, but are interested in the content? Register anyway and you will receive access to the recording!

Who should attend?

Professionals involved in or interested in clinical trials who want to understand how to interpret the results of common statistical analyses and have more effective communications with their statistical team members.

Learning Objectives

• Determine why certain statistical analyses are applied to specific outcomes
• Evaluate statistical summaries and extract the important information
• Interpret the statistical results using easily understandable language

Component Type: Forum
Level: Advanced
CE: ACPE 1.00 Knowledge UAN: 0286-0000-25-576-L04-P; CME 1.00; RN 1.00

The benefit-risk assessment of a new medicinal product or intervention is crucial through all stages of its development and ultimately leading to regulatory approval. This process can be complex, as it involves trade-offs between often conflicting multiple efficacy and safety endpoints, along with different methodologies for assessing benefits and risks. Therefore, clearly and transparently describing the benefit-risk profile and communicating the trade-offs using all available evidence is essential for regulatory decision-making and individual patient management. Bayesian inference, in addition to conventional approaches, offers a natural framework for conducting quantitative assessments of the benefit-risk trade-off. It allows for the formal use of prior information and the integration of various sources of information and uncertainty, while also linking to optimal decision theory. With a growing focus on improving the process of benefit-risk assessment at the FDA, sponsors are increasingly making efforts to apply quantitative benefit-risk assessments, often utilizing a Bayesian framework. This session will present innovative Bayesian methods for benefit-risk assessment, along with empirical examples. Industry and regulatory experts will reflect on their diverse research experiences with Bayesian benefit-risk methods, highlighting its strengths, limitations, and potential future applications. The impact of these methodological advancements as evident in stakeholders' enhanced ability to make informed decisions regarding benefit-risk assessments, ultimately contributing to improved patient safety and healthcare outcomes will be shared. General recommendations for planning and implementation of Bayesian benefit-risk assessments, including regulatory perspectives, will touch upon all areas of the structured benefit-risk assessment process starting from defining key outcomes and eliciting preference information through to the final integrated analysis of benefits and

Learning Objectives

Explain the critical importance of quantitative benefit-risk assessment and its impact; Describe how Bayesian methods can provide a natural framework for such quantitative assessments, along with empirical examples; Discuss strengths, limitations and general recommendations, including regulatory perspectives, for planning and implementation of Bayesian benefit-risk assessments.

Chair

Madhurima Majumder, PhD

Speaker

Speaker
Bennett Levitan, MD, PhD

Speaker
Saurabh Mukhopadhyay, PhD

Speaker
Sai Dharmarajan, PhD

Component Type: Session
Level: Intermediate
CE: ACPE 1.00 Application UAN: 0286-0000-25-591-L04-P; CME 1.00; RN 1.00

In this session, speakers will share their insight with case studies to demonstrate how the challenges have been addressed and introduce new methods for designing appropriate wining criteria for composite endpoint in small-sized clinical trials.

Learning Objectives

Recognize the pros/cons and challenges in designing clinical trials with composite endpoints; Discuss advanced methods and strategies for analyzing composite endpoints, including the impact of winning criteria and analysis methods via win ratio, win proportion and win scores; Apply different win ratio methods by scenarios and interpret results from real case examples of small-size.

Chair

Xiaoyu Cai, PhD

Speaker

Utilizing Win Ratio Approaches and Two-Stage Enrichment Designs for Small-Sized Clinical Trials
Jialu Wang, PhD

Application of Win Statistics in Non-Malignant Hematology
Wenquan Wang, PhD

Weighted Win Score Ratio – A Generalization of Win-Ratio Approach for Composite Endpoints with Continuous Components
Qian Tang, PhD

Component Type: Session
Level: Intermediate
CE: ACPE 1.00 Knowledge UAN: 0286-0000-25-606-L04-P; CME 1.00; RN 1.00

Externally controlled clinical trials emerges as a viable alternative to traditional RCTs but come with risks such as confounding and bias. This session will focus on discussing these challenges and how to mitigate them via design and analysis.

Learning Objectives

Identify and describe various sources of bias in externally controlled clinical trials, and illustrate their impact on causal effect estimation; Explain statistical methods developed to estimate causal treatment effect from externally controlled trials; Discuss regulatory perspectives on the use of external controls for drug development and registration.

Chair

Xiang Zhang, PhD

Speaker

Externally Controlled Trials in Oncology: Regulatory Considerations and a Case Example
Arup Sinha, PhD

Panelist
Charles Lee, MBA, MS

An EU Regulatory Perspective on External Controls
Andrew Thomson, PhD, MA, MS

Uncovering the Challenges of Utilizing RWE in Regulatory Decision Making: Barriers, Mitigations and Future Directions
Ran Duan, PhD

Component Type: Session
Level: Intermediate
CE: ACPE 1.00 Knowledge UAN: 0286-0000-25-634-L04-P; CME 1.00; RN 1.00

Aftificial Intelligence (AI) is being adopted across the medicine lifecycle to increase insights into data and improve efficiency of processes, for the benefit of public and animal health. This session will address key elements to enable the safe and responsible use of AI.

Learning Objectives

Identify AI opportunities across the medicine lifecycle; Examine the legal ecosystem of AI across jurisdictions and identify opportunities for convergence; Discuss how regulators worldwide are developing guidance to support AI innovation in the medicine lifecycle; State other strategic AI initiatives, including approaches to leveraging AI with healthcare data.

Chair

Luis Pinheiro, PharmD, MSc

Speaker

Panelist
Timothe Menard, PharmD, MSc

Panelist
Tala Fakhouri, PhD, MPH

Component Type: Session

Join the Clinical Research Community for a follow up round table discussion tied to session: Statistical Challenges and Innovations in Small-Sized Clinical Trials Using Composite Endpoint (Tuesday, June 17 | 1:45pm - 2:45pm EDT). Space is limited.

Learning Objectives

Identify ways to apply concepts and techniques from the session.

Chair

Noel Ellison, MS

Component Type: Session
Level: Basic
CE: ACPE 1.00 Knowledge UAN: 0286-0000-25-650-L04-P; CME 1.00; RN 1.00

In the rapidly evolving landscape of medical product development, the utilization of synthetic data and digital twins has gained increasing interest as a transformative approach to accelerate the development and approval of medical products. This session aims to explore the potential and innovative applications of these technologies in the realm of drug development, featuring speakers from health authorities, academia and industry. Dr. Khaled El Emam, Professor at the University of Ottawa, will present his latest research on generating synthetic data to augment clinical trials. Additionally, he will explore practical applications of synthetic data generation using real-world data. Dr. Arman Sabbaghi, Associate Professor at Purdue University, with prior experience at Unlearn, will present AI-Generated Digital Twins to deliver more efficient clinical trials. He will discuss the innovative statistical methodologies and novel trial designs that combine historical data, artificial intelligence (AI), and randomization to deliver smaller, faster, and more powerful RCTs that are built with regulatory guidance in mind. Dr. Ye Li, Mathematical Statistician, CDER, FDA will present statistical challenges leveraging machine learning in clinical trials. Several key factors that may influence the application of ML and AI algorithms in analyzing efficacy data will be discussed, including estimand and type I error rate control, as well as the importance of interpretability and reproducibility of ML/AI models to ensure robust and reliable results. Furthermore, she will address corresponding considerations related to these factors when using Digital Twins.

Learning Objectives

Describe what synthetic data and digital twins are; Discuss ways to apply AI to generate synthetic data and digital twin; Illustrate the potential of synthetic data and digital twins in medical product development.

Chair

Di Zhang, PhD

Speaker

Applications of Synthetic Data in Clinical Trials and Real-World Studies
Khaled El Emam, PhD

Statistical Methods for Unleashing AI-Generated Digital Twins to Deliver More Efficient Randomized Controlled Trials
Arman Sabbaghi, PhD, MA

Leveraging Machine Learning in Clinical Trials: Statistical Challenges
Ye Li, PhD

Component Type: Forum
Level: Intermediate
CE: ACPE 1.00 Knowledge UAN: 0286-0000-25-666-L04-P; CME 1.00; RN 1.00

Regulatory, industry, and academic organizations discuss modern approaches to clinical drug development by describing novel methods and use cases integrating real-world data and AI to generate real-world evidence, with focus on oncology and rare diseases.

Learning Objectives

Discuss global regulatory guidance and frameworks supporting innovative designs using real-world data as well as approaches to inclusion of artificial intelligence (AI); Describe innovative approaches and methods through examples of applied AI use in drug development; Discuss areas of research interest and promote community collaboration.

Chair

Donna Rivera, PharmD, MSc, FISPE

Speaker

Applying Artificial Intelligence in Drug Development
Sid Jain, MBA

Use of Artificial Intelligence Approaches for Rare Diseases
Christina Mack, PhD, MPH

Component Type: Session
Level: Advanced
CE: ACPE 1.00 Knowledge UAN: 0286-0000-25-665-L04-P; CME 1.00; RN 1.00

In this session, we will provide a brief overview of the evolving ICH E20 effort with the goal to provide a transparent and harmonized set of principles for the design, conduct, analysis, and interpretation of adaptive clinical trials.

Learning Objectives

Discuss considerations specific to adaptive design using Bayesian methods; Recognize opportunities and challenges of using Bayesian methods in the regulatory setting.

Chair

Amy Xia, PhD

Speaker

E20 Updates
Amy Xia, PhD

FDA Bayesian Updates
Mark Rothmann, PhD

Bayesian Trial Design Case Studies
John Zhong, PhD

Component Type: Forum
Level: Intermediate
CE: ACPE 1.00 Knowledge UAN: 0286-0000-25-660-L04-P; CME 1.00; RN 1.00

This session will provide an opportunity for key stakeholders, including regulators, product developers and patients to discuss best practices around patient experience data, including planning, collecting, and submitting such data to regulators.

Learning Objectives

Discuss current expectations for regulator/sponsor interactions related to patient experience data that will be used in regulatory submissions; Recognize common pitfalls for submission of patient experience data in regulatory applications, and how to avoid them.

Chair

Robyn Bent, BSN, MS, RN

Speaker

Panelist
Pujita Vaidya, MPH

Panelist
Brett Hauber, PhD, MA

Component Type: Session
Level: Intermediate
CE: ACPE 1.00 Knowledge UAN: 0286-0000-25-673-L04-P; CME 1.00; RN 1.00

Alternative methods to a frequentist approach, such as Bayesian, may be superior in certain situations; particularly, when analyzing trial data to support approvals in small populations. Regulators are increasingly considering novel methods.

Learning Objectives

Examine global considerations for leveraging Bayesian approaches in pivotal trials to support marketing authorization; State and debate the situations and circumstances where the Bayesian approach may be the most appropriate method of analysis; Discuss health authority considerations that could facilitate the use of alternative analytical methods beyond the frequentist approach; Identify ways to bridge existing gaps in applying Bayesian methods in clinical research.

Chair

Philip Hougaard, DrSc, PhD

Speaker

FDA perspective
Yun Wang, PhD

EMA Perspective
Andrew Thomson, PhD, MA, MS

Industry Perspective
Scott Berry, PhD