DIA 2019 Global Annual Meeting

Component Type: Session
Level: Intermediate
CE: ACPE 1.00 Knowledge UAN: 0286-0000-19-522-L04-P; CME 1.00; IACET 1.00; RN 1.00

This session addresses the concern of inconsistent real world evidence in drug safety by presenting 1) epidemiological approaches for assessing inconsistent findings in observational studies, 2) lessons learned from observational studies in a distributed research network and 3) challenges using methods that synthesize findings from observational studies.

Learning Objectives

Identify possible study factors contributing to heterogeneity of real world evidence using claims databases; Discuss lessons learned from observational studies in a distributed research network regarding heterogeneity of RWE; Describe methods for synthesizing findings from observational studies and their limitations.

Chair

Michael Blum, MD, MPH

Speaker

Epidemiological Approaches for Assessing Inconsistent Findings in Observational Studies
Huei-Ting Tsai, PhD, MS

Minimizing Heterogeneity in Distributed Research Networks: Examples from the Canadian Network for Observational Drug Effect Studies
Adrian Levy, PhD, MSc

Challenges with Methods Used to Synthesize Findings from Observational Studies
Alicia Gilsenan

Component Type: Session
Level: Intermediate
CE: ACPE 1.00 Knowledge UAN: 0286-0000-19-524-L04-P; CME 1.00; IACET 1.00; RN 1.00

Learn 1) challenges in adapting AI in risk management; 2) approaches in proactively managing clinical operation risk and in streamlining the process for automation; 3) a automation model to transform resource based monitoring into risk based approach

Learning Objectives

Discuss challenges in adapting artificial intelligence in risk management; Discuss innovative approaches in leveraging real-time access to clinical data and analytics in proactively managing clinical operation risk and in streamlining the process for automation in risk assessment and mitigation; Discuss automation maturity model to transform resource based monitoring into risk based approach.

Chair

Hanming H Tu, MSc

Speaker

Using Artificial Intelligence to Centralize, Standardize, and Ensure Data Integrity in Clinical Trial Monitoring
Michelle Marlborough

Artificial Intelligence in Risk Management
Susan Olson, PhD, MS

Utilizing AI to Transform Drug Safety Case Processing
Christina J Wocosky

Component Type: Session
Level: Intermediate
CE: ACPE 1.00 Application UAN: 0286-0000-19-531-L04-P; CME 1.00; IACET 1.00; RN 1.00

For slowly progressed diseases, it may take years, if not decades, to observe clinical outcomes, such as mortality. Owing to the regulatory agencies’ accelerated approval pathway, drug developers can now consider applying for a drug approval based on surrogate endpoints. In this session, many surrogate endpoints adopted in various diseases and statistical methods for determining the validity of the surrogacy will be discussed, including their implementation in real case examples.

Learning Objectives

Describe cases that utilize the accelerated approval and what good surrogate endpoints are for common and rare diseases; Identify how to apply appropriate statistical methods for assessing the association between surrogate endpoints and clinical outcomes.

Chair

Yeh-Fong Chen, PhD

Speaker

FDA Update
Aloka Chakravarty, PhD

Statistical Assessment of Potential Surrogate Endpoints: Is the Bar too High?
Marc E. Buyse, DrSc

Using the Biomarker Endpoint for Accelerated Approval
Stephanie O. Omokaro, MD

Component Type: Session
Level: Intermediate
CE: ACPE 1.00 Knowledge UAN: 0286-0000-19-540-L04-P; CME 1.00; IACET 1.00; RN 1.00

Standardizing real world data, consisting of EHR and claims encounters, into a clinical trial endpoint relies on a complex process of selecting the appropriate data-set and filtering, mapping and merging healthcare encounters from EHR to trial endpoints.This session will describe and appraise approaches and challenges in formulating the endpoints, in both prospective and retrospective research projects, with a focus on cardiovascular endpoints as well as demographic data.

Learning Objectives

Describe process to extract clinical trial endpoints from EHR and claims datasets; Identify how to apply a process to supplement objective EHR and claims dataset endpoints with correlated patient-reported outcomes.

Chair

Debra Fasteson Harris

Speaker

Phenotypic Diagnosis of Rare or Difficult to Diagnose Diseases
Richard Gliklich, MD

PRO Perspective not Typically Available in EHR or Other Large Existing Real World Datasets
Amanda Harrington

Component Type: Session
Level: Intermediate
CE: ACPE 1.00 Knowledge UAN: 0286-0000-19-564-L04-P; CME 1.00; IACET 1.00; RN 1.00

ICH E6 (R2) guidance stresses the importance of centralized monitoring, especially by leveraging statistical analyses in order to assess data quality. This session will discuss how statistical methodology fits in the overall quality risk management strategy for the trial portfolio.

Learning Objectives

Discuss centralized statistical monitoring approaches and their impacts to data quality and integrity.

Chair

Cheng Su, PhD

Speaker

Using Appropriate Count-Based Statistical Models for KPIs, Risk Metrics, QTLs, etc.
Michael Farnum, PhD, MS

ICH E6 R2: How Statistics Can Help Improve Data Quality
Stephen Young, MSc

Centralized Statistical Monitoring: A Large Pharma's Approach
Taras Carpiac, MBA

Component Type: Session
Level: Basic
CE: ACPE 1.00 Knowledge UAN: 0286-0000-19-593-L04-P; CME 1.00; IACET 1.00; RN 1.00

This session will discuss the recommendations of the Blue-Ribbon Commission, an overview of CDISC 2.0, the innovation initiative CDISC is currently undertaking to and what benefits these efforts will bring for all stakeholders.

Learning Objectives

Describe CDISC 2.0 and how this initiative and what benefits these efforts will bring for all CDISC stakeholders; Describe how beginning to end connected standards can streamline clinical data management make and data sharing easier.

Chair

Kit Howard, MS

Speaker

CDISC 360 and CDISC Library: Continuing the Journey Toward Beginning-to-End Automation
Bess LeRoy, MPH

SEND: What's New Today and on the Horizon for CDISC's Standard for Nonclinical Research
Lou Ann Kramer

CDISC Primer: Making CDISC Easier: A Collaboration Between CDISC and PhUSE
Kit Howard, MS

Component Type: Session
Level: Intermediate
CE: ACPE 1.25 Knowledge UAN: 0286-0000-19-629-L04-P; CME 1.25; IACET 1.25; RN 1.25

Artificial Intelligence (AI) and Machine Learning ML) have become buzzwords in every industry. While most of AI's potential is yet to be reached, pharma is starting to see tangible benefits that can be applied across the industry. This session will discuss new technologies and methods leveraging AI and ML to transform aspects of MedDRA coding, WHO drug coding, and data quality.

Learning Objectives

Explain and contrast the definitions of Artificial Intelligence and Machine Learning; Describe how AI has impacted MedDRA coding; Discuss how to review ways in which AI has been implemented to improve the efficiency and accuracy of WHO drug coding and the challenges experienced along the way; Identify how pharmaceutical companies and CROs have leveraged machine learning technology to improve data quality and overall drug submission success.

Chair

Stacey Yount, MA

Speaker

Introduction to Artificial Intelligence and Machine Learning: Application for MedDRA Coding
Cartic Ramakrishnan, PhD

How Can We Optimize the Use of Machine Learning for Increased Efficiency in Drug Coding?
Malin Jakobsson, MPharm, RPh

The Use of Machine Learning to Transform Clinical Trial Data Quality and Efficiency
Stacey Yount, MA

Component Type: Session
Level: Intermediate
CE: ACPE 1.25 Application UAN: 0286-0000-19-647-L04-P; CME 1.25; IACET 1.25; RN 1.25

Panelists will share experience and methods that make the integration of EHR and clinical trial electronic data capture a reality that can be repeated – and without protocol-specific custom coding.

Learning Objectives

Describe the difference between electronic medical record (EMR) systems and electronic health record systems (EHR); Define the key principles and methods for integrating medical record data with electronic data capture; Evaluate integration feasibility, scalability and costing for EHR and EDC/eSource data integrations.

Chair

Gene Vinson

Speaker

Sponsor Perspectives on EHR to EDC Integration
Len Rosenberg

Technology Innovations for EHR Data Integrations
Hugh Levaux, PhD

Operational Lessons Learned for EHR to EDC Integrations
Gene Vinson

Component Type: Session
Level: Intermediate
CE: ACPE 1.00 Knowledge UAN: 0286-0000-19-665-L04-P; CME 1.00; IACET 1.00; RN 1.00

The value of eSource is indisputable. TransCelerate member companies and other select organizations will share experiences of eSource implementation and discuss lesson learned to continue to progress towards the digitalization of clinical trials.

Learning Objectives

Discuss the application of eSource in clinical trials from an industry member consortium and others;Identify important lessons learned for future application and adoption of eSource within their organizations and continue to uncover unknowns in this emerging space related to technology and standards in need of further advancement.

Chair

Rakesh Maniar, MS

Speaker

Digital Health Data Strategy Playbook : An Approach to the Implementation of Novel Digital Technologies in Clinical Trials
Louisa Xu

Combining Multiple Device Data to Understand Disease States
Nora Husani

Workflows for eSource
Ken Bengston

Direct Data Collection: Eliminating Paper Source in a Paperless Pediatric Trial - A Case Study
Jennifer Price

Component Type: Session
Level: Advanced
CE: ACPE 1.00 Knowledge UAN: 0286-0000-19-669-L04-P; CME 1.00; IACET 1.00; RN 1.00

Best practices for development and maintenance of IRT systems will be discussed. Examples of systems that were inadequately developed resulting in significant impacts on study conduct, data quality, and data reliability will be presented.

Learning Objectives

Describe the importance of ensuring that interactive response systems (IRT) used to conduct clinical trials are adequately validated and maintained; Discuss how errors resulting from IRT systems impact study conduct, data quality, and data reliability.

Chair

Jean M. Mulinde, MD

Speaker

FDA Perspective
Phillip D. Kronstein, MD

MHRA Perspective
Stephen Vinter

Component Type: Session
Level: Advanced
CE: ACPE 1.25 Knowledge UAN: 0286-0000-19-689-L04-P; CME 1.25; IACET 1.25; RN 1.25

The abundance of data collected in clinical research offers an increasing opportunity for insights into risks, quality, and compliance.To realize the full value, the data must be harnessed in a manner that is useful and meaningful, which can be a challenge. The key lies in developing analytics and models that provide insights. Leveraging data is particularly valuable in an environment where Risk Based Monitoring (RBM) is now the norm and the models and dashboards derived from analytics can proactively indicate risks and issues. Interpretation can drive compliance and quality decision making and a focus on what matters most. Advanced analytics and machine learning can also be leveraged to detect irregularities that may indicate falsification and misconduct.

This session will explore the potential of data analytics and modeling as applied to risk-based monitoring activities as well as quality assurance oversight of risk-base monitoring.

Learning Objectives

Design an innovative and efficient sampling SDV method reflecting trends of data error occurrence; Assess how measured risk changes over time, comparing change in risk relative to operational attributes, and judging the effects of risk-based monitoringon risk mitigation; Define key changes to risk-based monitoring programs that may require changes to quality assurance oversight, including audit programs; Discuss how site audits may shift focus from on-site audit activities to remote.

Chair

Matthew Krumrai

Speaker

Statistical Models of Risk Flux Reveal Dynamics of Risk-Based Monitoring Managed Clinical Trials
Kristin Stallcup, MS, PMP

Study on Designing a Method of Sampling SDV in a Clinical Trial Using Audit Trail Data
Yuhi Sakajiri

Subject Data Analysis and Potential Fraud and Misconduct Analysis for Data Quality in Clinical Trials
Wei Xue, DrPH

Maintaining Quality Assurance in a Risk-Based Monitoring Environment
Matthew Krumrai

Component Type: Session
Level: Intermediate
CE: ACPE 1.25 Knowledge UAN: 0286-0000-19-701-L04-P; CME 1.25; IACET 1.25; RN 1.25

Demonstrating the quality of real world data (RWD) to support regulatory decision-making is a topic of great interest. This session will explore what is being done to demonstrate that RWD is of sufficient quality to support regulatory assertions.

Learning Objectives

Describe how sponsors and data companies can demonstrate data quality, how FDA defines data quality, and how FDA reviewers assess real world evidence data quality; Discuss challenges with demonstrating data quality and what can be done to make advances in this area.

Chair

Rebecca Lipsitz, PhD

Speaker

Evaluating Real World Data and Evidence
David Martin, MD, MPH

Considerations for Selecting an Appropriate Real World Data Source
Brande Yaist, MHS

Aspects of Data Relevancy and Quality for Regulatory-Grade Real World Data
David Thompson, PhD

Real World Evidence Regulatory Grade Quality: Lessons from Recent Use Cases
Rebecca A Miksad, MD, MPH

Component Type: Session
Level: Intermediate
CE: ACPE 1.25 Knowledge UAN: 0286-0000-19-716-L04-P; CME 1.25; IACET 1.25; RN 1.25

This session will discuss how to promote utilization of innovative methodologies such as M&S and Bayesian approach for efficient drug development. Regulators will present their recent activities for reviewing/qualifying innovative methodologies and share their experiences.

Learning Objectives

Identify how to recognize innovative methodologies recently applied for efficient new drug development and related activities in regulatory agencies aiming at accelerating drug development; Discuss how to consider the use of such methodologies in drug development through better communication between industry and regulatory agencies.

Chair

Yuki Ando, PhD

Speaker

Utilization and Evaluation of Innovative Methodologies: PMDA Perspective
Yuki Ando, PhD

Extrapolation: Recent European Regulatory Considerations
Andrew Thomson, PhD, MA, MS

Model-Based Bayesian Methods in Pharmaceutical Clinical Trials
Neal Thomas, PhD

Component Type: Session
Level: Basic
CE: ACPE 1.25 Knowledge UAN: 0286-0000-19-719-L04-P; CME 1.25; IACET 1.25; RN 1.25

See the latest electronic submission metrics for regulatory submissions and conformance with study data standards. Learn about FDA's current challenges with building upon existing automation in submission processing and how you can help.

Learning Objectives

Discuss the status of electronic submissions and validation at the FDA; Identify key components of lifecycle management for electronic submissions; Apply proposed best practices for handling daily submissions and ensuring validation standards are followed.

Chair

Tessa Brown, BSN, DrSc, MPH, RN

Speaker

FDA Study Data Standards Submission Update
Heather Crandall, MA

FDA Electronic Submissions Update
Jonathan Resnick, PMP