David L. DeMets Lectures

DeMets Lectures

The Lectures

The David L. DeMets Lectures in Health and Quantitative Investigation are a pair of lectures held on a Thursday and Friday each November.

These lectures acknowledge Dr. DeMets’ research accomplishments in forwarding the design, monitoring, analysis, and presentation of clinical trials.

The first lecture is a research/overview talk is accessible to a general biomedical/public health audience. Ideally, it will highlight in one or more ways critical and impactful contributions from quantitative methodological areas (e.g., biostatistics and/or biomedical informatics) to discoveries in biomedicine, and/or advancement of human/public health. The second lecture is a world-class research talk in biostatistics or biomedical informatics.

The lecturer chosen each year exemplifies the “David DeMets Model of Biomedical Research” with rigorous development and application of quantitative methodological principles, married with deep engagement in an area of biomedical investigation.

2026 DeMets Lectures

Lecture 1

Thursday, November 12, 2026

Health Sciences Learning Center, Room 1306

4:00-5:00 pm

In-person only

Title: With Reference to What?

Abstract: Goals and criteria, either explicit or implicit, guide much of statistical practice. In this context, I discuss statistical considerations associated with inference to a reference population, and those guided by a goal or a criterion. Reference population examples come from clinical trials, epidemiology and surveys, with “representation” an overarching issue. Goal/criterion guided examples are drawn from summarizing laboratory assays and from hospital profiling. I close with comments on a related administrative and political issue: the what and the how.

Lecture 2

Friday, November 13, 2026

Morgridge Hall Seminar Room (7560 Morgridge Hall)

Time: 12:00-1:00 pm.

In-person only

Title: Improved Small Domain Estimation via Compromise Regression Weights

Abstract:

Shrinkage estimation of small domain parameters typically combines a noisy direct estimate with a more stable regression estimate. However, if the regression model is misspecified, the “optimal” (MLE) estimate of regression parameters can degrade performance for the relatively unstable domains due to substantial shrinkage toward the misspecified regression surface. Jiang and co-authors addressed this issue via the Observed Best Predictor (OBP) that, in estimating the regression, over-weights the small domains, thereby giving more weight to those that, “care about” the regression. Going a step further, we introduced a class of empirically-informed regression weights that are a convex combination of the MLE and OBP values–a Compromise Best Predictor (CBP). The mixing parameter is found by minimizing an unbiased estimate of the mean-squared prediction error, a SURE estimate. This approach enables the CBP to preserve the robustness of the OBP while retaining the main advantages of the MLE when the regression model is well-specified. We compute performance of the MLE, OBP and CBP approaches via simulation, and compare data analysis results in estimating gait speed in older adults.

2026 DeMets Lecture Speaker

Thomas A. Louis, PhD
Professor Emeritus, Department of Biostatistics
Johns Hopkins Bloomberg School of Public Health

Dr. Louis is Professor Emeritus in the Department of Biostatistics at Johns Hopkins Bloomberg School of Public Health. Dr. Louis earned his PhD in Mathematical Statistics from Columbia University, after which he was an NIH Postdoctoral Fellow in Mathematics at Imperial College, London.

Dr. Louis has served on the faculty of several universities, including in the Department of Mathematics at Boston University, Biostatistics at Harvard University, Biostatistics in the Harvard School of Public Health, the University of Minnesota School of Statistics and School of Public Health, and in Biostatistics at the Johns Hopkins Bloomberg School of Public Health, as well as the RAND Corporation and US Census Bureau. His research interests include environmental, health and public policy and development of related statistical procedures. Methods research concentrates on Bayesian modeling, the analysis of observational studies, and research synthesis. Current applications of this work include assessing the health effects of airborne particulate matter, clinical quality improvement, cardio-pulmonary consequences of AIDS therapies, evaluation of teacher effectiveness, small area estimation, and statistical methods to assess environmental justice.

Dr. Louis has been elected as Fellow of the Faculty at Columbia University, an elected member of the International Statistical Institute, Fellow of the American Statistical Association and the American Association for the Advancement of Science (AAAS), National Associate of teh National Research Council, Honorary Life Member of the International Biometric Society, and to the Delta Omega Honorary Public Health Society. He has served as President of the Eastern North American Region (ENAR) of the International Biometric Society and as President of the International Biometric Society.