Abstract Number:
1308
Submission Type:
Invited Paper Session
Participants:
Julian Wolfson (1), Theresa Kim (2), Rebecca Hubbard (3), Theresa Kim (2), Sherri Rose (4), Fan Li (5), Jared Huling (1), Jose Zubizarreta (6)
Institutions:
(1) University of Minnesota, N/A, (2) National Institutes of Health, National Institute on Aging, N/A, (3) University of Pennsylvania, N/A, (4) Stanford University, N/A, (5) Duke University, N/A, (6) Harvard University, N/A
Chair:
Theresa Kim
National Institutes of Health, National Institute on Aging
Co-Organizer:
Theresa Kim
National Institutes of Health, National Institute on Aging
Discussant:
Session Organizer:
Speaker(s):
Session Description:
Data derived from electronic health records, insurance claims, and other administrative sources are increasingly used to generate so-called "real-world evidence" about treatment and policy decisions in health systems. Such data can provide more insight into how healthcare is delivered "on the ground" than traditional designed studies, but generating trustworthy evidence from these data requires careful consideration of data completeness, quality, and judicious statistical methods to reduce biases due to confounding, misclassification, and sampling.
While health systems and government regulatory bodies have begun to recognize the usefulness of real-world data, enthusiasm has been tempered by disagreements between the findings of real-world studies and randomized trials. For example, a recent paper aiming to predict the findings of a randomized trial of a new diabetes drug based on real-world data generated significant controversy when its estimate of the drug's efficacy on some outcomes was markedly lower than the trial's published results. There is also concern that decisions based on evidence derived from systems which already generate disparate outcomes across population subgroups may reinforce and perhaps even worsen health disparities.
To improve the trustworthiness of real-world evidence, there is a need to adapt existing methods to handle the scale and complexity of real-world data and to create novel approaches to address new questions. This invited session will highlight the work of field leading methodological researchers who actively apply their work to improve health care decision-making using real-world data.
In order to offer the opportunity for speakers to share their work and also to encourage a robust discussion, our session will feature three (or four) 20-minute research talks and a 15-minute presentation by a discussant. The remaining 15-30 minutes of the session will consist of a moderated Q&A session between the speakers and session attendees. The invited speakers and their proposed presentations topics are:
Dr. Sherri Rose (Professor, Health Policy, Stanford University) will discuss how her colleagues and she have created network models and fairer algorithms for multiple minoritized groups in a high-impact chronic kidney disease study.
Dr. Fan Li (Professor, Statistical Science, Duke University) will discuss where covariate adjustment can improve precision in analyzing randomized clinical trials, including several recent advancements in theory, methods, and software, while addressing the existing barriers.
Dr. Jared Huling (Assistant Professor, Biostatistics, University of Minnesota) will discuss how learning health systems use internal data and experiences to promote continuous improvement and innovation in health care delivery.
Dr. Jose Zubizarreta (Professor, Health Policy, Harvard University) will discuss how innovative statistical matching procedures can be used to obtain more robust estimates of health care use and outcomes.
Dr. Rebecca Hubbard (Professor, Biostatistics, University of Pennsylvania) will identify common themes in the speaker's presentations and pose questions that will "seed" the audience Q&A session with the speaker panel.
This session is timely, as the number of statisticians working with health systems data is growing rapidly. We also believe that the diversity and prominence of our speaker lineup will attract a large and broad audience.
Sponsors:
ENAR 3
Health Policy Statistics Section 1
Society for Medical Decision Making 2
Theme:
Statistics and Data Science: Informing Policy and Countering Misinformation
Yes
Applied
Yes
Estimated Audience Size
Medium (80-150)
I have read and understand that JSM participants must abide by the Participant Guidelines.
Yes
I understand and have communicated to my proposed speakers that JSM participants must register and pay the appropriate registration fee by June 1, 2024. The registration fee is nonrefundable.
I understand