Location: Innovation Hall, room E100 + MS Teams.
Remote viewing: A link is sent 24h before the talk to subscribers of the STATATUVM ListServ.
3:30 PM ET
Title: An illusion of predictability in scientific results: Even experts confuse inferential uncertainty and outcome variability
Abstract: Traditionally, scientists have placed more emphasis on communicating inferential uncertainty (i.e., the precision of statistical estimates) compared to outcome variability (i.e., the predictability of individual outcomes). Here, we show that this can lead to sizable misperceptions about the implications of scientific results. Specifically, we present three preregistered, randomized experiments where participants saw the same scientific findings visualized as showing only inferential uncertainty, only outcome variability, or both and answered questions about the size and importance of findings they were shown. Our results, composed of responses from medical professionals, professional data scientists, and tenure-track faculty, show that the prevalent form of visualizing only inferential uncertainty can lead to significant overestimates of treatment effects, even among highly trained experts. In contrast, we find that depicting both inferential uncertainty and outcome variability leads to more accurate perceptions of results while appearing to leave other subjective impressions of the results unchanged, on average.
3:30 PM ET
Title: Feel the noise: a statistical journey through the resolution of a low frequency noise issue
Abstract: In nature, low-frequency noise (LFN) exposure is typically short-term and often serves as a warning (e.g., thunder). Modern society has sources of LFN that are pervasive, leading to prolonged exposure to this stressor. The WHO refers to LFN as an invisible toxin. I will discuss some of the challenges and successes of a project to remediate LFN. My interest in LFN research began six years ago due to some health issues. I was very fortunate to be able to join a team of acoustical and mechanical engineers to help design and implement a solution. The fact that everyone experiences sound differently, along with the clash between experimental design and practical considerations of operating a central heating plant, added to the complexity. In collaboration with the institution, I took the lead on writing the RFP that was used to hire two acoustical engineering firms to assess and remediate the source of the LFN. My talk will cover statistical aspects of the journey to a successful resolution of the LFN issue.
3:30 PM ET
North Carolina State University
Title: Fractional Ridge Regression
Abstract: Ridge regression was introduced by Hoerl and Kennard (1970), and twenty-six years later was followed by the introduction of the lasso Tibshirani (1996). The body of research ensuing from these seminal papers is staggering and has contributed immensely to our understanding of shrinkage and selection methodology and to the practice of regression modeling in many areas of science. In some applications of regression modeling, the goal is simply to achieve the best possible predictions of future response values. In other applications, interpretation is important as a way to guide understanding of the process under investigation. Ridge regression is very good at prediction, although it is often eclipsed by the lasso in terms of both prediction and interpretation because the lasso also allows for selection.
The method introduced in this talk, fractional ridge regression, has the potential to improve both prediction (as measured by mean square error) and interpretability (as measured by the specificity of variable selection) relative to the lasso.
3:45 PM ET
University of Pennsylvania
Title: Opportunities and challenges for advancing health equity through electronic health records-based research
Abstract: Vulnerable populations, including racial and ethnic minorities and medically frail individuals, are under-represented in randomized clinical trials (RCTs), raising concerns about health equity and the external validity of results. Additionally, clinical trials may not reflect care and outcomes as they are experienced in routine practice and can be slow to provide timely evidence in the face of rapidly evolving or urgent public health or medical crises. Electronic health records (EHR) have the potential to address some of these concerns. However, limitations of EHR necessitate careful attention to study design and application of appropriate statistical methods. In this talk, I will discuss the potential of EHR data to supplement RCTs and support risk-guided medical decision-making by providing both more timely and generalizable evidence and presenting statistical approaches for addressing the challenges of conducting research in EHR data. Methodological approaches will be illustrated using real-world studies from the cancer care continuum. Through judicious application of appropriate methods, EHR have the potential to support more equitable health research, but careful consideration must be given to the limitations of this data source.
3:45 PM ET
University of Vermont
3:45 PM ET
University of Hong Kong
Past seminars can be found here