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.

Linear models. Learn the theory behind linear models. Gain hands on experience with real data from a variety of disciplines. The course will focus on the statistical computing language R.

Statistical learning. Learn the theory behind cutting edge statistical and machine learning techniques. Gain hands on experience with real data from a variety of disciplines. The course will focus on the statistical computing language R.

Linear models. Learn the theory behind linear models. Gain hands on experience with real data from a variety of disciplines. The course will focus on the statistical computing language R.

Traditional measures of vaccine efficacy (VE) are inherently asymmetric, constrained above by 1 but unbounded below. As a result, VE estimates and corresponding confidence intervals (CIs) can extend far below zero, making interpretation difficult and potentially obscuring whether the apparent effect reflects true harm or simply statistical uncertainty. The proposed symmetric vaccine efficacy (SVE) is a bounded and interpretable alternative to VE that maintains desirable statistical properties while resolving these asymmetries.

Objectives Electronic health record (EHR) data are prone to missingness and errors. Previously, we devised an enriched chart review protocol where a “roadmap” of auxiliary diagnoses was used to recover missing values. Still, chart reviews are expensive and time-intensive, limiting the number of patients whose data can be reviewed. Now, we investigate the accuracy and scalability of a roadmap-driven algorithm, based on International Classification of Diseases, 10th revision (ICD-10) codes, to mimic expert chart reviews and recover missing values.

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.