Real-world evidence (RWE) studies that utilize existing healthcare data to evaluate treatment effects are subject to multiple sources of bias. While confounding and data quality are often considered the major challenges in RWE generation, it has been shown that many observational studies yield biased results because of self-inflicted and avoidable errors in study design. 

Learning Objectives:  

• Discuss major sources of avoidable bias in RWE studies of treatment effects. 
• Describe strategies that will help the audience identify such bias. 
• Discuss current approaches to avoiding self-inflicted bias when designing RWE studies.  

Target Audience: Any are welcome. 

Presenter: Katsiaryna Bykov, Brigham and Women’s Hospital and Harvard Medical School  

Moderator: Mugdha Gokhale, Pfizer 

The ISPE student council and RWE collaborative special interest group (SIG) are coming together with four pharmacoepi experts in academia, regulatory, and industry to discuss their careers. Listen to the journeys of successful professionals and bring your questions!

Learning Objective: Discuss different paths in RWE collaborative careers

Target Audience:  Any and all are welcome.

Presenters:

Shirley V. Wang, PhD – Associate Professor, Harvard Medical School

Wei Zhou, MD, PhD,  - Associate Vice President and Head of Oncology, Value, Evidence, and Outcomes (VEO), Eli Lilly and Co

Patrice Verpillat, MD, MPH, PhD – Head of the Real World Evidence (RWE) Workstream, European Medicines Agency (EMA)

Moderator:

Liz Mansi, ISPE student council chair

SISPE presents a webinar on CVs/Resumes and job applications featuring experts Daniela Moga (University of Kentucky) and Jenny Sun (Pfizer). Dr. Moga will discuss crafting effective CVs, teaching and research statements, and academia interview strategies. Dr. Sun will share valuable insights on industry applications, translating academic CVs into professional resumes, and excelling at industry interviews. The goal of this webinar is to enhance pharmacoepidemiology job application skills with practical advice from professionals in academia and industry.

The simultaneous rise in demand for global RWD and proliferation of potential data sources and analytic tools require pharmacoepidemiologists to understand and collaborate on data strategy. This four-part video series on data strategy, including governance, quality management, architecture, and asset planning was co-developed by ISPE members and Donna Burbank of Global Data Strategy.

The simultaneous rise in demand for global RWD and proliferation of potential data sources and analytic tools require pharmacoepidemiologists to understand and collaborate on data strategy. This four-part video series on data strategy, including governance, quality management, architecture, and asset planning was co-developed by ISPE members and Donna Burbank of Global Data Strategy.

Presented by 

Dr. Celine Chui (Hong Kong), Dr. Masao Iwagami (Japan), Prof Edward Lai (Taiwan), Dr. Judy Shin (South Korea)

This webinar aims to provide an in-depth understanding of the electronic health records (EHR) databases in Hong Kong, Japan, Taiwan, and South Korea. Participants will gain valuable insights into the unique features of each database, including Hong Kong's CDARS (Clinical Data Analysis and Reporting System), Japan's MDV (Hospital-based database provided by Medical Data Vision Co., Ltd) and JMDC (claims data provided by JMDC Inc.), South Korea's HIRA (Health Insurance Review and Assessment Service) and NHID (The National Health Information Database), and Taiwan's NHIRD (National Health Insurance Research Database) and CGRD (Chang Gung Research Database). Presenters from each region will introduce these databases and showcase research done using these databases.

Learning Objectives:

1. Provide an overview of electronic health record databases in Asia with potential for international collaboration.

2. Foster cross-continental international collaboration.

The simultaneous rise in demand for global RWD and proliferation of potential data sources and analytic tools require pharmacoepidemiologists to understand and collaborate on data strategy. This four-part video series on data strategy, including governance, quality management, architecture, and asset planning was co-developed by ISPE members and Donna Burbank of Global Data Strategy.

Presented by Andrew Bate, VP, Head of Safety Innovation & Analytics, GSK

Despite the hype, AI/ML is not new: it’s been used routinely in elements of Pharmacoepidemiology and Safety for decades. For all the talk of experimentation and related methodological publications, the biggest challenges are often operationalizing these activities into routine use. This webinar will focus on challenges and insights into how operationalizing barriers can be overcome, and will include examples of how AI/ML has been or is being incorporated to support routine safety surveillance activities. 

Learning Objectives:

• Recognize what AI is and the opportunities it affords for pharmacoepidemiology and routine safety surveillance activities
• Understand how AI is impacting safety now and how it may do so in the future
• Appreciate some of the implications of AI on the field

Presented by Janick Weberpals, Transparency and reproducibility in pharmacoepidemiology research 

Transparency and reproducibility in conducting healthcare database studies in pharmacoepidemiology are critical scientific requirements for meaningful research. While many advances have been made in the documentation and reporting of study protocols and results, the principles for version control and sharing of analytic code in real-world evidence are not yet as established as in other quantitative disciplines like computational biology and health informatics. 

This tutorial aims to give a practical introduction tailored for pharmacoepidemiologists on how to set up, structure, and implement analytic workflows using Git, the most frequently used distributed version-control system to date. 

Machine learning has the potential to transform aspects of how healthcare and medicine are delivered, yet we know these technologies have the capacity to exacerbate existing inequalities (or introduce new ones). 

This webinar will discuss what it means for machine learning algorithms to be fair, explore potential issues, and share concrete steps for creating fair algorithms, as part of our larger goal of promoting equity in health systems. Special attention will be paid to fairness and bias considerations for large language models (e.g., ChatGPT/GPT-4, Bard/LaMDA).