DIA 2021 Virtual Global Annual Meeting

Component Type: Session
Level: Intermediate

Having a product approved does not guarantee uptake post approval. This session will provide recommendations to insert evidence generating diagnostic strategies earlier in clinical development and will discuss case studies on different tactics.

Learning Objectives

Identify areas of opportunity to insert diagnostic strategies to generate real-world evidence into clinical development plans; Assess existing clinical development plans for new therapeutics and integrate evidence collection to support post approval product uptake.

Chair

Earl Seltzer, MBA

Speaker

Right the First Time: Integrating Diagnostic Strategies into Clinical Development Plans
Patrick Short

Right the First Time: Integrating Diagnostic Strategies into Clinical Development Plans
Keith Hanigan

Right the First Time: Integrating Diagnostic Strategies into Clinical Development Plans
Jeffrey Shuster, PhD

Component Type: Session
Level: Intermediate

Aggregated clinical safety evaluation is a continual and iterative process. Well-designed observational study and advanced statistical analysis in integrating randomized clinical trials (RCTs) and real-world data (RWD) can be used to answer regulatory safety question, both pre- and post- marketing.

Learning Objectives

Describe the potential role for randomized clinical trials (RCTs) and real-world data (RWD) in safety decision-making; Discuss the opportunities and challenges in aggregated clinical safety evaluation with both real world and clinical trial data sources.

Chair

Li-An Lin, PhD

Speaker

Industry Perspective
Ranjeeta Sinvhal, MD

Integrative Analysis of Randomized Clinical Trial and Real-World Study
Li-An Lin, PhD

Advanced Machine Learning Methods for Casual Inference
Birol Emir, PhD

Panelist
Walter Straus, DrMed, MD, MPH, FACP

Component Type: Session
Level: Advanced

Responding and adapting to the changes invoked by the COVID-19 pandemic on clinical research foster new opportunities in many aspects of clinical research. At the heart of these opportunities are the need for statistical and analytical innovation. This session will highlight several areas where statistical and analytical innovation have informed our response to the COVID-19 pandemic and what this means for the future of clinical research. There will be a talk on the use of artificial intelligence for the rapid identification and determination of potential therapeutics that have the best chance of treating against those infected with the SAR-COV-2 virus. Another talk will discuss how the COVID-19 pandemic has magnified the vulnerability and unsustainability of the current clinical development which emphasizes the need to move towards more pragmatic, patient-centric, and adaptive trial designs that make extensive use of computer technology. The final talk will discuss how analytics and data visualization foster an ability to make timely decisions that impact the healthcare sector and clinical development.

Learning Objectives

Describe an artificial intelligence/machine learning approach that can be used to identify candidate treatments; Describe the immediate, and potential long-term, impact of the COVID-19 pandemic on the clinical-research enterprise, both in terms of trials in this disease as well as non-COVID-19 trials; Assess how data and analytics can be used to respond to public health challenges.

Chair

Mat Soukup, PhD

Speaker

Fighting COVID-19 with Trusted Analytics
Mark Lambrecht, PhD

Randomized Controlled Trials (RCTs): Lessons from COVID-19
Marc E. Buyse, DrSc

Industry Update
Sherrine Eid, MPH

Component Type: Session
Level: Intermediate

Innovative approaches to pediatric drug development are needed to reduce the delay between adult and pediatric marketing approval. This session presents a cross-company pediatric platform trial in Crohn's disease using a Bayesian analysis.

Learning Objectives

Identify some of the recruitment challenges in pediatric drug development; Identify the role of platform trials in addressing these challenges; Discuss the clinical, scientific, regulatory, and operational challenges in designing and implementing a cross-company pediatric platform trial.

Chair

Robert Nelson, MD, PhD

Speaker

Industry Update
Laurie Conklin, DrMed, MD

Industry Update
Wendy Komocsar

Industry Update
Forrest Williamson, PhD

Component Type: Session
Level: Intermediate

This session will present an overview of the challenges with assessment methods and analytics in heterogeneous populations with case studies focusing on dermatology assessments in people of color and on methods to address heterogeneity in rare disease populations.

Learning Objectives

Identify the challenges that heterogeneity poses in clinical trial research; Describe assessment methods to address phenotypic heterogeneity to improve accurate data capture; Discuss analytic methods to address heterogenous manifestations of signs, symptoms, and impacts, which are a challenge in rare diseases.

Chair

Sonya Eremenco, MA

Speaker

The Importance of Accounting for Diversity When Training Clinicians to Apply Dermatological Rating Scales in Clinical Trials
Alexandra Kalpadakis, PhD, MSc

Multi-Domain Responder Index: A Novel Analysis Tool to Capture Broad Clinical Benefit in Heterogeneous Complex Rare Diseases
P. K. Tandon, PhD

Component Type: Session
Level: Advanced
CE: ACPE 1.00 Knowledge UAN: 0286-0000-21-523-L04-P; CME 1.00; IACET 1.00; RN 1.00

Expand your statistical toolkit by learning some innovative approaches that will lead to improved interventions for patients.

Learning Objectives

Discuss how to pave the way towards personalized medicine with generalized pairwise comparisons; Identify how to predict patient-reported outcomes after hip and knee replacement surgeries with examples in super learning; Describe the implications of using recurrent episodes in a rare disease setting.

Chair

Matt Baldwin, MS

Speaker

Predicting Patient-Reported Outcomes After Hip and Knee Replacement Surgeries: Examples in Super Learning
Maciej Maruszczak, MSc

Implications of Using Recurrent Episodes: Statistical Considerations in a Rare Disease Setting
Hengrui Sun, DrPH, MD

Generalized Pairwise Comparisons: Paving the Way Towards Personalized Medicine
Vaiva Deltuvaite-Thomas, MS

Component Type: Session
Level: Advanced
CE: ACPE 1.00 Knowledge UAN: 0286-0000-21-528-L04-P; CME 1.00; IACET 1.00; PDU 1.00 PMI 21660EX90U; RN 1.00

This session will explore use of external intelligence data sources in advanced statistical modeling to inform program strategies and clinical designs. Case studies will be shared.

Learning Objectives

Describe various external data sources that can be used for statistical modeling; Identify how to use statistical modeling approaches leveraging this external data; Discuss how statistical modeling can inform and influence project strategies and clinical designs.

Chair

Mark Kryah, PMP

Speaker

Informing Drug Development via Landscape Modeling Using Pharma Intelligence Data
Zhaoling Meng, PhD, MS

Application of Advanced Modeling on Development Programs
Shameer Khader, PhD, MPH

Component Type: Session
Level: Intermediate
CE: ACPE 1.00 Knowledge UAN: 0286-0000-21-547-L04-P; CME 1.00; IACET 1.00; RN 1.00

This session will discuss the outputs from the DIA-ASA joint scientific working group which include an aggregate safety assessment planning tool, benefit risk assessment planning, and open source interactive graphical tools, and how to implement them.

Learning Objectives

Describe the importance of multidisciplinary collaboration; Evaluate the aggregate safety assessment planning tool; Discuss the status of benefit risk evaluation planning and how pre-marketing safety monitoring interconnects with benefit-risk evaluation; Identify open source interactive safety graphics tools with pragmatic guidance that can be easily applied to routine safety evaluation.

Chair

Mengchun Li, MD, MPA

Speaker

Aggregated Safety Assessment Planning
Barbara Hendrickson, MD

Benefit-Risk Planning Across the Industry and Best Practice Sharing
Brian Edwards, DrMed

Interactive Safety Graphics
James Buchanan, PharmD

Panelist
Lothar Tremmel, PhD

Component Type: Session
Level: Intermediate
CE: ACPE 1.00 Knowledge UAN: 0286-0000-21-554-L04-P; CME 1.00; IACET 1.00; RN 1.00

There were unprecedented challenges associated with conducting clinical trials during COVID-19. This session will explore how sponsors and HAs worked together to address these issues and continue efforts to develop medical products and the lessons learned from making those adaptations. The session will also consider adaptations that may be applicable to trials post-pandemic.

Learning Objectives

Describe how COVID-19 impacted randomized controlled trials (RCTs) including examples of deviations from study protocols; Discuss how sponsors interacted with Health Authorities to maintain awareness of protocol deviations and receive guidance on planned modifications; Identify the best practices that were learned and how we can apply them to future trials.

Chair

Lauren Hetrick

Speaker

Industry Update
David Hilfiker, MS

FDA Perspective
Leonard Sacks, MD

Industry Update
David Vulcano, MBA, RAC

Component Type: Session
Level: Intermediate
CE: ACPE 1.00 Knowledge UAN: 0286-0000-21-596-L04-P; CME 1.00; IACET 1.00; RN 1.00

This session will include details in which complex and innovative designs are available to use to answer which questions, the global regulatory acceptability of such designs, and how it may be possible to conduct a single global development even if regional differences exist.

Learning Objectives

Distinguish and describe complex innovative designs that are increasingly common in drug development; Discuss different regional views on the evaluation of such designs; Appraise and choose between different designs that answer complex questions.

Chair

Andrew Thomson, PhD, MA, MS

Speaker

FDA Update
James Travis, PhD

Industry Update
Heinz Schmidli, PhD, MSc

Industry Update
Scott M Berry, PhD

Component Type: Session
Level: Intermediate

Establishing a department to automate and inform clinical data services to reduce timelines with high quality and consistency requires infrastructure and personnel mix decisions to ensure capabilities for initial and future applications. We will present our learnings in building such a department, through the creation of a data warehouse, with unique design and security considerations that allows automation of operational tasks as well as the deployment of tools, applications, and visualizations.

Chair

Statistics & Data Corporation

Component Type: Session
Level: Intermediate
CE: ACPE 1.00 Knowledge UAN: 0286-0000-21-610-L04-P; CME 1.00; IACET 1.00; RN 1.00

We will share experience with Model Informed Drug Development (MIDD) Pilot Program over the past three years from the perspective of FDA and pharma industry experts including a discussion of key learnings, best practices, and impact on the development and regulatory strategy.

Learning Objectives

Summarize experience with the Model Informed Drug Development (MIDD) Pilot Program over the past three years from FDA and pharma industry perspective; Evaluate detailed MIDD case studies from experts in the field including key learnings and impact on the development and regulatory strategy; Discuss best practices for the future.

Chair

Bonnie Brennan, PharmD

Speaker

Experience and Impact of Model-Informed Drug Development (MIDD) Pilot Program: FDA Perspective
Rajanikanth Madabushi, PhD

Prediction of Cytokine Release and Active Dose of a CD3-CD28-CD38 Trispecific Antibody for Multiple Myeloma: Utilization of a Systems Pharmacology Platform for Multispecific T Cell Engagers
Spyros Stamatelos, PhD

Industry Update
Patrick Smith, PharmD

Component Type: Session
Level: Intermediate

Complex laboratory methods are commonly used to evaluate critical quality attributes of drugs in development and throughout the product lifecycle. Instruments have validated ranges for obtaining data and results are commonly obtained that are beyond the limits of detection. New techniques appropriately deal with limits of detection and control for extrapolation within statistical models. Use of such techniques yield significant improvements in the precision and accuracy of model estimates.

Chair

SAS Institute Inc. JMP Division

Component Type: Session
Level: Intermediate
CE: ACPE 1.00 Knowledge UAN: 0286-0000-21-624-L04-P; CME 1.00; IACET 1.00; RN 1.00

The COVID-19 pandemic has led us to change the way that site-based assessments have been conducted. This session explores the impact of mixing modes, the use of analytics methods to assess data consistency and how/when to use sensitivity analyses.

Learning Objectives

Describe the data integrity considerations associated with mixing modes, and review current evidence on the impact of mixing modes; Identify the value of analytics methods to assess data consistency resulting in mixing modes of data collection; Discuss how and when to use formal sensitivity analyses in studies where mixing modes was implemented as a result of COVID-19.

Chair

Bill Byrom, PhD

Speaker

Measurement Equivalence of Clinical Outcome Assessments Collected Across Mixed Modes
Paul O'Donohoe, MS, MSc

Analytics Approaches to Assessing Ongoing Clinical Endpoint Data Consistency
Alan Kott, MD

General Regulatory View and Sensitivity Analyses Topic
Andrew Potter, PhD

Component Type: Session
Level: Intermediate
CE: ACPE 1.00 Knowledge UAN: 0286-0000-21-645-L04-P; CME 1.00; IACET 1.00; RN 1.00

Artificial intelligence technology is now being used to support decision-making in clinical trials and medical devices. This session will discuss fairness, bias, and how to respond to discrimination and security failures in predictive modeling systems.

Learning Objectives

Describe the different types of fairness metrics and algorithms for detecting and mitigating unintended machine learning algorithmic bias; Discuss a standardized workflow that focuses on artificial intelligence/machine learning model interpretability, post-hoc explanations, and discrimination testing.

Chair

David O Olaleye, PhD, MSc

Speaker

Application of Fairness and Bias Detection Metrics to a Real-World Clinical Prediction Problem
David O Olaleye, PhD, MSc

Real-World Experiences Helping Healthcare Companies Prepare For and Respond to Liabilities that Arise from Discrimination, Privacy, and Security Failures in Predictive Modeling Systems
Patrick Hall, MS

Disparities in Medication Access and Compliance: The Role of Machine Learning
Toyin Tofade, PharmD, MS

Component Type: Session
Level: Intermediate
CE: ACPE 1.00 Knowledge UAN: 0286-0000-21-660-L04-P; CME 1.00; IACET 1.00; RN 1.00

Predictive models generated by machine learning can exhibit inequitable behavior, with differing false positive rates between protected groups. We will describe how this type of bias and inequity can arise in models used for healthcare triage. In the second talk, we evaluate and assess how targeted learning and other state of the art and tools available for data-adaptive learning can be used to estimate and compare causal inference models. We will explore real-world observational data challenges, including missing data, confounding, and other issues. Using causal tools, from propensity scores to targeted learning, we will investigate this rich frontier of statistical science.

Learning Objectives

Describe machine learning algorithmic bias and how to evaluate predictive models for equity; Identify the state of the art and tools for data-adaptive learning and applications to causal inference and machine learning models.

Chair

David O Olaleye, PhD, MSc

Speaker

Incorporating Context and Causation in Observational Real World Data: From Propensity Scores to Targeted Learning
Andrew Wilson, PhD, MS

Algorithmic Bias in Healthcare Triage
Eric Siegel, PhD, MS

Representation of Diverse Groups in Test Sets
Terri L. Cornelison, MD, PhD

Component Type: Session
Level: Intermediate
CE: ACPE 1.00 Knowledge UAN: 0286-0000-21-664-L04-P; CME 1.00; IACET 1.00; RN 1.00

To promote health equity and reduce disparities in racial and ethnic minorities, this session will discuss potential trial designs and statistical methods to increase the participation of minorities and/or diversity of populations in clinical trials.

Learning Objectives

Discuss considerations and strategies in clinical trials' planning and conduct to enhance population diversity; Describe statistical challenges in clinical trial subgroup disparities; Identify potential trial designs and methodologies to mitigate disparity factors in the patient population.

Chair

Freda Cooner, PhD

Speaker

Considerations on the Use of Analytics and Technology to Enhance Diversity in Clinical Trials
Demissie Alemayehu, PhD

Disparities by Demographic Subgroups in Clinical Trials Submitted to the FDA
Laura Fernandes, PhD

Panelist
Ruthie Davi, PhD, MS