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Symmetric vaccine efficacy
Traditional measures of vaccine efficacy (VE) are inherently asymmetric, constrained above by 1 but unbounded below.
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Symmetric Vaccine Efficacy: Interpretable Estimation and Inference for Vaccine Trials
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Using large language models to enhance clinically-driven missing data recovery algorithms in electronic health records
Objectives Electronic health record (EHR) data are prone to missingness and errors.
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Mind the Gap: Causal Inference is Not Just a Statistics Problem
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Causal Inference in R
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Causal Inference in R
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Miss(ing) Congeniality: The Cost of Incompatible Imputation Models
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The Role of Congeniality in Multiple Imputation for Doubly Robust Causal Estimation
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Causal Inference in R
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Causal Inference in R
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STA 312 -- WFU Spring 2026
Linear models.
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The Role of Congeniality in Multiple Imputation for Doubly Robust Causal Estimation
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The Art of Data Refinement: Severance Analyses
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Exploring the Potential of Large Language Models in Generating Saturated DAGs for Causal Inference
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Causal Inference Is Not Just A Statistics Problem
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Causal Inference in R
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Causal Inference in R
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Immaculate vibes, questionable code
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Building Strong ASA Student Chapters: Strategies for Growth, Inclusivity, and Maximizing Value
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STA 363 -- WFU Fall 2025
Statistical learning.
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STA 312 -- WFU Fall 2025
Linear models.
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Understanding Statistics in Medical Literature
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Understanding Statistics in Medical Literature
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Partitioned Local Depth (PaLD) Community Analyses in R
Partitioned Local Depth (PaLD) is a framework for holistic consideration of community structure for distance-based data.
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Quantifying the Alignment of a Data Analysis between Analyst and Audience
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Using Mathlink Cubes to Introduce Data Wrangling with Examples in R
This article explores an innovative approach to teaching data wrangling skills to students through hands-on activities before transitioning to coding.
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One Simple Way to Get Better at Reading Data
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The Case for Deterministic Imputation in Predictive Modeling
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Combining Straight-Line and Map-Based Distances to Investigate the Connection Between Proximity to Healthy Foods and Disease
Healthy foods are essential for a healthy life, but accessing healthy food can be more challenging for some people than others.
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The Why Behind Including Y in your Imputation Model
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Untangling Causal Effects: Understanding the Limits of Statistics
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Data Jamboree: A Party of Open-Source Software Solving Real-World Data Science Problems
The evolving focus in statistics and data science education highlights the growing importance of computing.
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It's ME hi, I'm the collider it's ME
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Including the outcome in your imputation model -- why isn't this 'double dipping'?
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Power and sample size calculations for testing the ratio of reproductive values in phylogenetic samples
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The Art of the Invite: Crafting Successful Invited Session Proposals
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Evaluating the Alignment of a Data Analysis between Analyst and Audience
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Why You Must Include the Outcome in Your Imputation Model (and Why It's Not Double Dipping)
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Causal Inference in R
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Causal Inference in R
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Statistical Rigor in Academic Medicine: Practical Strategies for Improvement
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A Framework for Developing AI-powered Negotiation Agents to Examine Producer-Consumer Dynamics in Data Science
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Partnering with Authors to Enhance Reproducibility at JASA
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When to Include the Outcome in Your Imputation Model: A Mathematical Demonstration and Practical Advice
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Bridging the Gap Between Theory and Practice: When to Include the Outcome in Your Imputation Model
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The 'Why' behind including 'Y' in your imputation model
Missing data is a common challenge when analyzing epidemiological data, and imputation is often used to address this issue.
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Power and sample size calculations for testing the ratio of reproductive values in phylogenetic samples
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.
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Bridging the Gap Between Imputation Theory and Practice
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Causal Inference is Not Just a Statistics Problem
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Integrating Design Thinking in the Data Analytic Process