Recent content on Lucy D'Agostino McGowan
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)…
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…
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,…
This article explores an innovative approach to teaching data wrangling skills to students through hands-on activities before transitioning to coding. Data wrangling, a critical aspect of data analysis, involves cleaning, transforming, and restructuring data. We introduce the use of a physical tool, mathlink cubes, to facilitate a tangible understanding of datasets. This approach helps students…
Healthy foods are essential for a healthy life, but accessing healthy food can be more challenging for some people than others. This disparity in food access may lead to disparities in well-being, potentially with disproportionate rates of diseases in communities that face more challenges in accessing healthy food (i.e., low-access communities). Identifying low-access, high-risk communities for…
The evolving focus in statistics and data science education highlights the growing importance of computing. This paper presents the Data Jamboree, a live event that combines computational methods with traditional statistical techniques to address real-world data science problems. Participants, ranging from novices to experienced users, followed workshop leaders in using open-source tools like…
Missing data is a common challenge when analyzing epidemiological data, and imputation is often used to address this issue. Here, we investigate the scenario where a covariate used in an analysis has missingness and will be imputed. There are recommendations to include the outcome from the analysis model in the imputation model for missing covariates, but it is not necessarily clear if this…
The quality of the inferences we make from pathogen sequence data is determined by the number and composition of pathogen sequences that make up the sample used to drive that inference. However, there remains limited guidance on how to best structure and power studies when the end goal is phylogenetic inference. One question that we can attempt to answer with molecular data is whether some people…
This paper introduces a collection of four data sets, similar to Anscombe’s Quartet, that aim to highlight the challenges involved when estimating causal effects. Each of the four data sets is generated based on a distinct causal mechanism: the first involves a collider, the second involves a confounder, the third involves a mediator, and the fourth involves the induction of M-Bias by an…