Traditional measures of vaccine efficacy (VE) are inherently asymmetric, constrained above by 1 but unbounded below. As a result, intervals for VE can extend far below zero, making interpretation challenging and sometimes giving a false impression of evidence that a vaccine is harmful when uncertainty is large. This talk proposes symmetric vaccine efficacy (SVE), a bounded and interpretable alternative to VE that maintains desirable statistical properties while resolving these asymmetries. SVE is defined as a symmetric transformation of observed infection proportions, ensuring estimates remain within (-1, 1) and providing a consistent scale for both beneficial and harmful vaccine effects. We derive a variance and confidence interval for SVE, describe its relationship to traditional VE, and illustrate its application in real trial data. In the example, SVE yields more interpretable uncertainty intervals and clearer graphical summaries compared to standard VE. We then demonstrate open-source tools for computing estimates of SVE and corresponding confidence intervals, available in R through the sve package.

In this talk we will discuss some of the major challenges in causal inference, and why statistical tools alone cannot uncover the data-generating mechanism when attempting to answer causal questions. We will showcase the Causal Quartet, which consists of four datasets that have the same statistical properties, but different true causal effects due to different ways in which the data was generated. These examples illustrate the limitations of relying solely on statistical tools in data analyses and highlight the crucial role of domain-specific knowledge.

This workshop introduces the essential elements of answering causal questions in R. Participants will work through examples of causal inference workflows, learn when standard statistical methods are appropriate and when specialized causal methods are needed, and practice specifying causal questions using Directed Acyclic Graphs (DAGs). The workshop also covers fitting, diagnosing, and applying propensity score models through weighting and matching to estimate causal effects.

Introduction to Regression and Data Science. Learn to explore, visualize, model, evaluate, and communicate data in a reproducible manner. Gain hands on experience with real data from a variety of disciplines. The course will focus on the statistical computing language R.

Seminar in Mathematical Business Analysis. The main purpose of this seminar is to develop the capability to apply quantitative knowledge to real and ill-defined problems. It tries to bridge the gap between the theory of quantitative decision approaches such as management science/operations research, information systems, and statistics (now mainly collected in the Business Analytics field), with the application of these approaches to the solution of actual business problems.

Causal Inference. From Correlation to Causation. The goal of this course is to give students the skills needed to conduct analyses and communicate results when causality is the goal. Students will learn how to implement causal inference techniques including matching and weighting, evaluate assumptions, and conduct sensitivity analyses.

This paper provides clear and practical guidance on the specification of imputation models when multiple imputation is used in conjunction with doubly robust estimation methods for causal inference. Through theoretical arguments and targeted simulations, we demonstrate that if a confounder has missing data, the corresponding imputation model must include all variables appearing in either the propensity score model or the outcome model, in addition to both the exposure and the outcome, and that these variables must enter the imputation model in the same functional form as in the final analysis.

Partitioned Local Depth (PaLD) is a framework for holistic consideration of community structure for distance-based data. This paper describes an R package, pald, for calculating Partitioned Local Depth (PaLD) probabilities, implementing community analyses, determining community clusters, and creating data visualizations to display community structure. We present essentials of the PaLD approach, describe how to use the pald package, walk through several examples, and discuss the method in relation to commonly used techniques.