Quantify to Qualify: A Quantitative Bias Analysis Workshop to Bust Bias and Drive Trustworthy Data-Driven Decision Making in Real-World Evidence

Speaker(s)

Discussion Leader: Stephen Duffield, PhD, MD, National Institute for Health and Care Excellence, Liverpool, LAN, UK
Discussants: Xiaoliang Wang, PhD, MPH, Flatiron Health, Inc., New York, NY, USA; Grace Hsu, MSc, Cytel Inc., Waltham, MA, USA; Gregory Sampang Calip, PharmD, MPH, PhD, Global Epidemiology, AbbVie Inc., Chicago, IL, USA

Presentation Documents

PURPOSE:

Attendees will gain solutions for the challenge of bias with applications of quantitative bias analysis (QBA) in real-world data (RWD) studies. Participants will walk away with a list of methods for applying QBA to inform real-world evidence (RWE) use cases including early drug development decision making and high-quality submissions to HTA bodies.

DESCRIPTION:

RWD/RWE is increasingly used to inform decision making in pharmacoeconomic and health outcomes research. However, RWD studies often involve risk from multiple sources of bias (selection bias, measurement errors, and unmeasured confounding), which may lead to erroneous conclusions if unaccounted for. QBA provides a principled approach to quantify the effects of these biases. However, QBA is not routinely implemented in RWD studies, partially due to limited knowledge or accessibility of the methods. Thus, it is essential for researchers to understand how to implement QBA for high-quality RWE studies. This will be useful for researchers working across therapeutic areas answering questions ranging from natural history to comparative-effectiveness.

Speakers will share pragmatic use cases for QBA at various stages of drug development and decision making. Based on his experience as a NICE scientist, Dr. Duffield will discuss the potential value and challenges of using different QBA results in HTA decision making. Dr. Wang will provide insights into systematically quantifying measurement errors that can impact a RWD study’s conclusion. Ms. Hsu will provide examples using QBA methods to address challenges with real-world synthetic control arms in assessing comparative effectiveness. Dr. Calip will present applied QBA use cases supporting decision-making in oncology early development, including bias analyses for contextual RWE in FDA diversity plans and orphan drug designation planning. Throughout the workshop, live-time polling and in-person and online questions-and-answers will be used for interactive discussions with the audience. The workshop will be relevant to industry and academic researchers, decision makers and methodologists.

Code

249

Topic

Methodological & Statistical Research