Chapter meeting -

Observational data underpin many biomedical and public-health decisions, yet they are easy to misread, sometimes inadvertently, sometimes deliberately, especially in fast-moving, polarized environments during and after the pandemic. This talk uses concrete COVID-19 and vaccine-safety case studies to highlight foundational pitfalls: base-rate fallacy, Simpson’s paradox, post-hoc/time confounding, mismatched risk windows, differential follow-up, and biases driven by surveillance and health-care utilization. 

Illustrative examples include:

1. Why a high share of hospitalized patients can be vaccinated even when vaccines remain highly effective. 

2. Why higher crude death rates in some vaccinated cohorts do not imply vaccines cause deaths. 

3. How policy shifts confound before/after claims (e.g., zero-COVID contexts such as Singapore), and how Hong Kong’s age-structured coverage can serve as a counterfactual lens to catch a glimpse of what might have occurred worldwide in 2021 if not for COVID-19 vaccines.

4. How misaligned case/control periods (e.g., a series of nine studies by RFK appointee David Geier) can manufacture spurious associations between vaccination and chronic disease. 

5. Why apparent vaccine–cancer links can arise from screening patterns rather than biology.

I will outline a design-first, transparency-focused workflow for critical scientific evaluation, including careful confounder control, sensitivity analyses, and synthesis of the full literature rather than cherry-picked subsets, paired with plain-language strategies for communicating uncertainty and robustness to policymakers, media, and the public. I argue for greater engagement of statistical scientists and epidemiologists in high-stakes scientific communication.

• The chapter meeting will be held at Moody Hall Auditorium, SMU. 

• The parking permit is available upon request.

Dr. Morris is the George S. Pepper Professor of Public Health and Preventive Medicine, and Professor of Biostatistics at the Perelman School of Medicine at the University of Pennsylvania, where he also serves as Director of the Division of Biostatistics, with over 35 primary faculty members.  He got his PhD in 2000 in Statistics from Texas A&M University working with Raymond J. Carroll and was a distinguished professor at University of Texas M.D. Anderson Cancer Center through 2019 when he moved to Penn, after having served as faculty at M.D. Anderson for almost 20 years.

Dr. Morris’ research interests focus on developing quantitative methods to extract knowledge from biomedical big data, including work to relate complex biomedical object data—including functions, images and manifolds—to patient outcomes and characteristics using flexible, automated regression methods, and to integrate information across multiple types of multi-platform genomic, proteomic, imaging, and wearable device data to uncover biomedical insights contained in these complex data. He has done extensive applied work in cancer research, including constructing novel prognostic indices for hepatocellular carcinoma and helping develop and characterize molecular subtypes of colorectal cancer to discover new precision therapeutic strategies.  He has also been active in scientific communication during and since the pandemic, trying to bring sound scientific principles to scientific discourse in the media and on social media.