DIA 2022 Global Annual Meeting

Component Type: Tutorial
CE: ACPE 2.75 Application UAN: 0286-0000-22-501-L04-P ; CME 2.75; IACET 2.75; RN 2.75

Preregistration required and is an additional fee. Already registered? Log in to DIA's My Account/My Events

People familiar with the design, conduct and analysis of clinical trials understand efficacy endpoints and their analyses, however the term estimand is not familiar to most non-statisticians. While many groups and large companies are moving forward and implementing ICH E9 R1 as well as incorporating estimands into their protocols, some companies have yet to understand or incorporate the components of an estimand in their trial design.

The final definition of an estimand in ECH E9 R1 is “A precise description of the treatment effect reflecting the clinical question posed by the trial objective. It summarizes at a population-level what the outcomes would be in the same patients under different treatment conditions being compared.“ and “An estimand is a parameter which is to be estimated in a statistical analysis. The term is used to more clearly distinguish the target of inference from the function to obtain this parameter (i.e., the estimator) and the specific value obtained from a given data set (i.e., the estimate).”

Regulatory agencies have not consistently asked for estimands in late-stage trials making it harder for industry to follow. What is needed is a way to simplify and effectively communicate the key concepts around the use of estimands and sensitivity analyses to non-statisticians.

This presentation will share the current status of industry’s understanding and adoption of estimands. Example estimands pertaining to diseases will be reviewed. The presentation will also suggest ways to improve understanding of estimands as statisticians communicate the concepts of ICH E9-R1 to non-statisticians.

Registration info: Rate: $350
Need approval in order to attend? Download and fill out our Justification Letter to demonstrate to your supervisor why this is a must-attend event.
Enhance your experience and register for two or more short courses at the same time and receive $50 in savings. 
Purchase must happen at same time. Discount will be reflected on the last page of the cart.

Upon completion of registration, participants will gain access to the following:

• Live Event Access
• Presentation Slides

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1.888.257.6457

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Who should attend?

Statisticians, non-statisticians, and physicians involved in clinical protocol review or development.

Learning Objectives

At the conclusion of this short course, participants should be able to:

• Discuss why the estimand framework and sensitivity analyses as described in ICH E9 are important for non-statisticians to understand
• Review example estimands and how one can apply the estimand framework to most Phase 2 and 3 studies
• Apply the Estimand framework and concepts to develop better protocols

Component Type: Tutorial
CE: ACPE 2.75 Application UAN: 0286-0000-22-502-L04-P ; CME 2.75; IACET 2.75; RN 2.75

Preregistration required and is an additional fee. Already registered? Log in to DIA's My Account/My Events

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 workshop, participants will be introduced to common statistical methods applied to specific outcomes and their results. Some examples of potential statistical concepts to be covered can include commonly seen hypothesis test, survival analyses, regression modelling, 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.


Download Registration Form
Registration Questions? Send Email
1.888.257.6457 Registration info: Rate: $350
Need approval in order to attend? Download and fill out our Justification Letter to demonstrate to your supervisor why this is a must-attend event.
Enhance your experience and register for two or more short courses at the same time and receive $50 in savings. 
Purchase must happen at same time. Discount will be reflected on the last page of the cart.Upon completion of registration, participants will gain access to the following:

• Live Event Access
• Presentation Slides

Return to DIA 2022

Who should attend?

This course would be of interest to those working in clinical trials with a non-statistical background who would like to gain a high-level understanding of statistical analyses and interpretation so they can communicate with their statistical colleagues more effectively.

Learning Objectives

At the conclusion of this short course, participants should be able to:

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

Component Type: Session
Level: Intermediate
CE: ACPE 1.00 Knowledge UAN: 0286-0000-22-513-L04-P ; CME 1.00; RN 1.00

Adaptive designs are accepted by regulatory agencies so why aren't more sponsors using them? These trials enable sponsors to fail faster and focus effort on where they succeed. We will cover the process from protocol design to interim analysis and communication to key stakeholders for adaptations. There are too many reasons why adaptive designs should be used on more clinical trials. This session will explore those reasons and benefits.

Learning Objectives

Identify the types of adaptive trials; Describe best practices and recommendations for the protocol design phase as well as pre-study planning; Identify what communication is needed to conduct the interim analysis; Assess which stakeholders should be part of this process; Discuss how success should be defined.

Chair

Marc Kaufman

Speaker

Using Trial Simulations to Provide Tailored Adaptive Design in Early Stage Clinical Trials
Song Wang, PhD, MS

Impact of Adaptive Trials to Global Clinical Supply Plans
Haneen S Mazahreh, MS

Adaptive Designs: CRO Perspective
Russell Reeve

Component Type: Session
Level: Advanced
CE: ACPE 1.00 Knowledge UAN: 0286-0000-22-521-L04-P ; CME 1.00; RN 1.00

ICH Q2 (R2) and Q14 - Is the juice worth the squeeze? In this session, subject matter experts will demonstrate how ICH 02 (R2) and Q14 can lead to stronger methods and enhanced flexibility.

Learning Objectives

Discuss what kinds of method development studies can help to support established conditions for the method and apply principles of current analytical procedure development; Describe how a risk assessment can be used to evaluate impact of a method change, and aid identification of reporting category; Identify science and risk-based approaches to analytical procedure lifecycle management.

Chair

Celeste Frankenfeld Lamm, PhD

Speaker

Managing the Analytical Procedure Lifecycle in the Future: Perspectives from ICH Q2(R2)/Q14
Nina S. Cauchon, PhD, RAC

ICH Q14: Analytical Procedure Development : Towards Risk-Based Lifecycle Management of Analytical Procedures – An EU Regulatory Perspective
Robert Bream, PhD, MA, MS

Lifecycle Management and Post-Approval Changes to Analytical Procedures: A Case Study
Christof Finkler, DrSc

Component Type: Session
Level: Intermediate
CE: ACPE 1.00 Application UAN: 0286-0000-22-518-L04-P ; CME 1.00; RN 1.00

Many disease-specific animal models have been used to test emergent medicines for rare diseases. Appropriate animal models, based on the knowledge of the molecular pathology of the human disease, are a significant element to support the medical plausibility of an orphan designation during the development of orphan medicines for these rare diseases. There is a body of evidence, however, that there is a substantial difficulty in choosing/accessing an optimal model or choosing measurements which would be truly informative of the product’s efficacy. We intend to present a critical revision of preclinical models that may be used to support orphan drug designations in rare infectious and neurologic diseases, which are validated for each disease and to evaluate assays pertinent to the core features of selected diseases or otherwise relevant from the clinical standpoint. The analysis will help identify models with best predictive value as well as those acceptable based on their face value, highlighting the areas of most unmet need where development of better preclinical tools is necessary. We will present also results from a study that has investigated the nature of data submitted to the Committee Orphan Medicines to support medical plausibility of orphan designations for a group of rare and paediatric neurological diseases, where the use of in vivo nonclinical data was significantly higher than clinical data. We will explore the data for several case studies and discuss the importance of the availability and publication of this information in encouraging sponsors to develop innovative medicines in rare infectious and neurologic conditions. Nonclinical appropriate models, assessing disease relevant endpoints, may contribute to increase the translational value of animal models in rare diseases, to accelerate research and the effective development of treatment options.

Learning Objectives

Discuss the importance and value of nonclinical efficacy animal models in rare diseases (neurological and infectious); Apply real-world experience from available historical data to the development strategy using animal models of disease towards an increase into the translational value of these models in drug development for rare diseases.

Chair

Martine Zimmermann, PharmD

Speaker

The Translational Value of Animal Models in Rare Disease: Regulator View
Dinah Duarte, MSc

Perspectives on Evaluating New Tools for Regulatory Use: Organ on a Chip Technology
Donna L. Mendrick, PhD

The Translational Value of Animal Models in Rare Diseases: Sponsor's Perspective
Melissa Haskell, DVM

Component Type: Session
Level: Intermediate
CE: ACPE 1.00 Application UAN: 0286-0000-22-522-L04-P ; CME 1.00; RN 1.00

Since the release of the ICH E9(R1) document in 2019, the estimand framework is becoming a fundamental part of clinical trial protocols. In addition, as missing data may occur due to various reasons, it is important to consider the different sources of missing data and appropriately account for these distinct sources in the statistical inference. In this session, we present the application of the estimand framework to three distinct and representative clinical trial settings: • The application to binary outcomes observed longitudinally, • The application to complex innovative designs (e.g. basket and platform trials); and • The application to outcomes measured by digital health technologies.

Learning Objectives

Discuss how different statistical methods perform in handling intercurrent events and missing data for binary outcome data observed longitudinally; Apply the estimand components in different types of complex innovative designs; Identify unique missing data challenges incurred from digital health technologies and strategies for minimizing missing data generated from them.

Chair

Mat Soukup, PhD

Speaker

Moderator
Munish Mehra, PhD, MS, MSc

Considerations to Address Missing Data When Deriving Clinical Trial Endpoints from Digital Health Technologies
Junrui Di, PhD

Estimands and Complex Innovative Designs
Olivier Collignon, PhD

Handling Intercurrent Events and Missing Data for Longitudinal Binary Data Under the Estimand Framework
Yunxia Sui, PhD

Component Type: Session
Level: Basic
CE: ACPE 1.00 Knowledge UAN: 0286-0000-22-537-L04-P ; CME 1.00; RN 1.00

This session will discuss how to maximize potential for informing regulatory decision making from real-world data by following the Targeted Learning Roadmap. Compare findings using TMLE+Super Learning with propensity score-based estimators. Develop a TL-based SAP.

Learning Objectives

Describe how to follow steps in the Targeted Learning (TL) Estimation Roadmap to extract reliable evidence from real-world data (RWD); Assess level of support for substantive conclusion; Recognize challenges of analyzing RWD, and how they can be mitigated by using data adaptive machine learning and TMLE; Access resources for developing a TL-based, completely pre-specified, statistical analysis plan.

Chair

Susan Gruber, PhD, MPH, MS

Speaker

Targeted Learning for Real World Evidence
Mark van der Laan, PhD

Regulatory Perspective
Hana Lee, PhD

Industry Perspective
Yixin Fang, PhD

Component Type: Session
Level: Basic
CE: ACPE 1.00 Knowledge UAN: 0286-0000-22-562-L04-P ; CME 1.00; RN 1.00

This session discusses challenges and considerations in the design and implementation of real-world evidence studies including registry-based pragmatic trials and external control studies, based on real applications.

Learning Objectives

Demonstrate various designs of a real-world evidence study; Identify the challenges to plan and conduct a real-world evidence study; Discuss considerations for those challenges.

Chair

Yueqin Zhao, PhD

Speaker

Regulatory Considerations on Leveraging Real World Evidence
Di Zhang, PhD

DAPA-MI: World’s First Indication-Seeking Registry-Based Randomized Controlled Trial
Jingyu (Julia) Luan, PhD

The Use of a Real-World Data as a Hybrid Control in PAH
Brian Hennessy, MSc

Component Type: Workshop
Level: Basic
CE: ACPE 1.00 Application UAN: 0286-0000-22-577-L04-P ; CME 1.00; RN 1.00

Group activities and slide-based learning will be used to introduce common statistical concepts that are used in the analysis of clinical trials in a simple, easy to understand manner for non-statisticians involved in clinical trials.

Learning Objectives

Explain basic statistical concepts; Identify the appropriate statistical analysis for a given outcome; Describe the interpretation of data via tabulated and graphical summaries; Compare the difference between statistical significance and clinical meaningfulness.

Chair

Stephen Corson, PhD

Speaker

Visualizing Clinical Trial Design and Results in 20 Graphical Displays: Achieving 20-20 in CSRs
Munish Mehra, PhD, MS, MSc

Component Type: Forum
Level: Intermediate
CE: ACPE 1.00 Knowledge UAN: 0286-0000-22-586-L04-P ; CME 1.00; RN 1.00

This forum will cover data science models that have been shared across companies, learnings from the testing and deployment of those models across companies, and prompt discussion on how further collaborative efforts could accelerate data-driven Q&C.

Learning Objectives

Discuss examples of R&D quality data science models that are developed and shared across companies; Discuss other sharing opportunities including Health Authority engagement, how to expand the collaborative efforts in order to drive data and digital transformation of R&D quality; Discuss potential to further advance risk-based and data-driven collaborative approaches.

Chair

Robert Studt

Speaker

Panelist
Jennifer Emerson, PhD, MPH, RN, PMP

Panelist
Michael Torok, PhD

Panelist
Ronojit Adyanthaya, MS

Component Type: Forum
Level: Advanced
CE: ACPE 1.00 Knowledge UAN: 0286-0000-22-589-L04-P ; CME 1.00; RN 1.00

Global leaders in pharmaceutical quality will discuss significance of revision of ICH guidelines on analytical procedures, stability, specifications, and risk assessment, impact to product lifecycle, and implementation.

Learning Objectives

Discuss ICH’s ongoing work to modernize fundamental CMC guidelines to support continual improvement and innovation in manufacturing technologies and approaches; Identify objectives of guideline revisions: Q9(R1) Quality Risk Management, guidelines on Stability, Q2 Analytical Validation, development of Q14 guideline on Analytical Procedure Development; Examine ICH’s multi-year strategy.

Chair

Amanda Marie Roache, MS

Speaker

Update on ICH Q9: Quality Risk Management
Stephen Mahoney, JD, MS

Overview and Discussion of Q14 Analytical Procedure Development and Revision of Q2(R21) Analytical Validation
Nina S. Cauchon, PhD, RAC

The ICH Quality Discussion Group (QDG): ICH as Enabler of Innovation in Global Quality
Ingrid Markovic, PhD

Component Type: Session
Level: Advanced
CE: ACPE 1.00 Knowledge UAN: 0286-0000-22-590-L04-P ; CME 1.00; RN 1.00

We focus on application of quantitative benefit-risk approaches in drug development and approval. Talks by experts in government and industry will demonstrate how to implement various analytic methods. Methodological considerations will be discussed.

Learning Objectives

Identify the value of quantitative benefit-risk approaches to address uncertainties and assess tradeoffs in complex drug approval decisions; Discuss considerations for implementing various quantitative benefit-risk approaches; Distinguish between different methods with respect to statistical properties, utility, and ease of implementation.

Chair

Sai Hurrish Dharmarajan, PhD

Speaker

Quantitative Benefit Risk Assessment at FDA/CDER:: Rivaroxaban for PAD
Leila Grace Lackey, MHS, PhD

Benefit Risk Assessment of COVID-19 Vaccine
Hong Yang, PhD

Benefit Risk Assessment
Zhong Yuan, MD, PhD

Benefit Risk Approaches
Kevin Marsh, PhD, MSc

Component Type: Forum
Level: Intermediate
CE: ACPE 1.00 Knowledge UAN: 0286-0000-22-599-L04-P ; CME 1.00; RN 1.00

The use of model-informed drug development (MIDD) has increasingly been incorporated into pediatric drug development programs. The ICH E11A guideline and the ICH MIDD discussion group will be used to open a discussion about different opportunities to use MIDD during drug development.

Learning Objectives

Describe current approaches to optimizing global drug development through quantitative methods such as model-informed drug development (MIDD) and pediatric extrapolation; Identify an update on the ICH E11A Pediatric Extrapolation guideline as well as an update from the ICH MIDD Discussion Group.

Chair

Solange Corriol-Rohou, DrMed, MD, PhD

Speaker

FDA Update
Lynne Yao, MD

EMA Update
Andrew Thomson, PhD, MA, MS

Industry Update
Nicolas Frey, PharmD

Component Type: Forum
Level: Basic
CE: ACPE 1.00 Application UAN: 0286-0000-22-606-L04-P ; CME 1.00; RN 1.00

This session for non-statisticians will demystify Bayesian statistics, introduce a framework for thinking about how and when Bayesian statistical approaches may be used in drug development, and present case studies demonstrating successful use and impact on clinical trial design.

Learning Objectives

Compare the fundamental difference between Bayesian and frequentist statistical thinking and how Bayesian statistical approaches are valuable in drug development; Discuss how to capitalize on opportunities within their companies and organizations for expanded application of Bayesian approaches and create strategies to overcome barriers to broader application.

Chair

Stephen Ruberg, PhD, MS

Speaker

Panelist
Jiawen Zhu, PhD

Panelist
Robert Hemmings, MSc

Panelist
Telba Irony, PhD, MS, MSc

Panelist
John Scott, PhD, MA

Component Type: Forum
Level: Intermediate
CE: ACPE 1.00 Knowledge UAN: 0286-0000-22-618-L04-P ; CME 1.00; RN 1.00

This session will feature international regulators describing the options open for having a Complex Innovative Design agreed. The focus will be on the success of the recent FDA pilot programme, and the current options available through the EMA.

Learning Objectives

Describe the aspects of FDA Complex Innovative Design and be able to foresee how to apply them to their own products; Recognize global differences in the pathways available and the possibility to achieve global harmonisation on complex innovative designs.

Chair

Andrew Thomson, PhD, MA, MS

Speaker

Moderator
Benjamin Hofner, PhD

FDA Update
Dionne Price, PhD

Sponsor Perspective on Interacting with the FDA CID Program: A Case Study
Marius Thomas, PhD

Component Type: Session
Level: Intermediate
CE: ACPE 1.00 Knowledge UAN: 0286-0000-22-631-L04-P ; CME 1.00; RN 1.00

Platform trials combine the advantages of multi-arm trials with the flexibility of classical drug development based on independent studies. We will highlight the main statistical issues and cover both the European regulatory and industry perspective.

Learning Objectives

Describe the main statistical challenges in platform trials; Discuss the role of clinical trial simulations when designing master protocols; Identify the key aspects when planning and analyzing platform trials.

Chair

Alex Sverdlov, PhD

Speaker

Master Protocols: Considerations from EU-PEARL and the European Regulatory Network
Benjamin Hofner, PhD

Challenges in the Execution of Platform Trials in the Pharmaceutical Industry and What we Have Learned from EU-PEARL and the COVID-19 Pandemic
Peter Mesenbrink, PhD

Academic Update
Martin Posch, PhD

Component Type: Session
Level: Advanced
CE: ACPE 1.00 Knowledge UAN: 0286-0000-22-645-L04-P ; CME 1.00; RN 1.00

The choice of endpoints for oncology trials requires balancing multiple and often competing objectives, like validity, feasibility, etc. In this session we’ll explore strategies for optimal endpoint selection from different perspectives.

Learning Objectives

Identify different estimands and attributes for efficacy endpoints in cancer clinical trials; Describe the different perspectives and trade-offs involved in the choice of endpoints; Discuss strategies for optimizing the choice of endpoint based on a survey of decision makers and examples of global cancer drug development.

Chair

Francesco Pignatti, MD

Speaker

Results of the EMA Survey on PFS as a Clinical-Benefit Endpoint
Douwe Postmus, PhD, MSc

Endpoint Selection Strategies to Address the Needs of Multiple Decision Makers Globally
Amy McKee, MD

Trial Objectives and Endpoint Selection Considerations in the Cancer Drug Development
Rajeshwari Sridhara, PhD

Component Type: Session
Level: Intermediate
CE: ACPE 1.00 Knowledge UAN: 0286-0000-22-655-L04-P ; CME 1.00; RN 1.00

This session will describe the science behind data visualization, in the context of the evolving regulatory landscape, and to introduce four interactive visualization tools addressing four common safety topics: hepatic, cardiac, renal toxicity and general adverse event data.

Learning Objectives

Identify how data visualization can assist safety assessment from both aggregated level and at individual level; Recognize the importance of multidisciplinary collaboration in data visualization; Evaluate novel open-source interactive visualization tools for safety assessment on hepatotoxicity, QT prolongation, renal toxicity and adverse event data; Discuss pragmatic approaches that can be applied in practice.

Chair

Mengchun Li, MD, MPA

Speaker

Visual Perception Concepts Important for Safety Assessment Tools
Susan Mayo, MS

Examples of Novel Visualization Tools for Safety Assessment Based on Multidisciplinary Collaboration
James Buchanan, PharmD

Examples of Novel Visualization Tools for Safety Assessment Based on Multidisciplinary Collaboration
Xiao Ni, PhD

Examples of Novel Visualization Tools for Safety Assessment Based on Multidisciplinary Collaboration
Jeremy Wildfire, MSc

Component Type: Session
Level: Intermediate

This session will introduce the importance of addressing Estimands from Protocol to CSRs using the ICHE9R1 guidance and CDISC standards.

Chair

Munish Mehra, PhD, MS, MSc

Component Type: Session
Level: Intermediate

We will illustrate some recent and unique progress of real-world evidence (RWE) studies in three directions: scientific research, clinical development and China market access.

Learning Objectives

Describe how multiple COVID-19 real-world evidence (RWE) papers were retracted and how experts have suggested criteria of how journal reviewers should evaluate real-world data (RWD)/RWE manuscripts; Discuss the most recent progress with the project RCT Duplicate as it has reached a milestone; Identify what the RWE heaven is as China has set a testbed for drug and medical device importing in Hainan Island.

Chair

Sheng Feng, PhD

Component Type: Session
Level: Basic

A picture is worth a thousand words. This session will introduce seven apps which enable the user to interactively display information of interest in the form of very powerful static and dynamic graphs.

Learning Objectives

Identify and interpret information of interest in an interactive way in daily statistical practice; Describe how to generate new insights in helping better understand the complex relationships under the clinical data and further drive the understanding of disease development, patient journey, and the impact of the treatments studied on the patient’s quality of life.

Chair

Erya Huang

Component Type: Session
Level: Intermediate

Data collected during routine care have variable quality and missingness. This panel outlines statistical and design approaches to analyzing and accounting for the effect of less-than-perfect data quality when reused for real-world evidence studies.

Learning Objectives

Identify inaccuracies, gaps, and informative missingness in patient records collected during clinical care; Describe how data quality can be evaluated and accounted for by novel statistical approaches when intended to be reused in real-world evidence (RWE) studies; Discuss advantages, challenges, and trade-offs in selecting datasets appropriate for RWE analyses.

Chair

Jan Horsky, PhD, MA

Component Type: Session
Level: Advanced

This session will present approaches to enhance endpoints in cancer trials from a clinical and patient perspective, in particular approaches that consider trade-offs between time spent in different disease and treatment states.

Learning Objectives

Describe opportunities for incorporating patient preferences and multi-state models into cancer drug development and evaluation; Discuss new approaches to enhance the patient-focus of traditional endpoints in oncology.

Component Type: Session
Level: Intermediate

There are many challenges facing the new drug development for rare diseases. Overcoming these challenges and therefore to achieve optimal drug development for rare disease require implementations of innovative design and analysis methods as well as strategic collaborations among regulatory, sponsors, physicians, institutions, and patients and patient groups. For this session, we will focus on one critical need in rare disease new drug development-endpoint selections.

Chair

Susan Wang, PhD

Component Type: Forum
Level: Intermediate

We review how ongoing study safety review process typically proceeds, focusing on some limits and how to get beyond them. With two examples we explore the limits further and propose a procedural and analytic framework to expand study safety reviews.

Learning Objectives

Review how safety monitoring is conducted for ongoing randomized studies; Describe the need for augmenting current individual patient-data review with aggregate reviews; Identify currently available methods for aggregate review and the need to incorporate their purpose and use into study data review plans; Discuss with the audience their experiences and reaction to proposals.

Chair

Ed Whalen, PhD

Component Type: Session
Level: Advanced

This session will bring representatives from FDA who issued most recent FDA Medical Queries (FMQs) and Standard Safety tables, listings, and figures (TFLs) as well as experts from US and Europe working on open source tools to graphically summarize data.

Learning Objectives

Describe unique graphs that succinctly summarize complex analyses; Discuss open source tools to visually inspect, analyze, and summarize data.

Chair

Munish Mehra, PhD, MS, MSc

Component Type: Workshop
Level: Basic

Group activities and slide-based learning will be used to introduce commonly used statistical concepts that are used in the analysis of clinical trials in a simple, easy to understand manner for anyone involved in clinical trials.

Learning Objectives

Explain basic statistical concepts; Identify the appropriate statistical analysis for a given outcome; Discuss assumptions and limitations of analysis methods with a study team, including Statisticians; Assess the clinical importance of statistical results.

Chair

Stephen Corson, PhD

Component Type: Session
Level: Intermediate

Traditional statistical methods used in drug development fit predefined models to existing data and then draw conculsions from it. Machine learning (ML)/artificial intelligence (AI) looks at finding complex patterns in data by using different statistical methods that those applied to date in drug development. This session will share some of the models used and success stories in ML/AI as applied to drug development.

Learning Objectives

Discuss the application of natural language processing (NLP) for extraction of summaries from clinicaltrials.gov, literature reviews, etc.; Describe CDRH's experience of review of devices and applications using ML/AI; Identify how to utilize historical clinical trials and real-world data to create knowledge that can maximize desired safety and efficacy results in future trials.

Chair

Munish Mehra, PhD, MS, MSc