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The Study of the Epidemiology of Pediatric Hypertension Registry (SUPERHERO): Rationale and Methods
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Analytic Design Theory: Framework for Alignment Between Analyst and Audience
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Causal Inference is not just a statistics problem
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.
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STA 112 -- WFU Spring 2024
Introduction to Regression and Data Science.
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BEM 392 -- WFU Spring 2024
Seminar in Mathematical Business Analysis.
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Moving Towards Best Practice: Dispelling Statistical Myths
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Data Jamboree: Analyzing NYC 311 Service Requests in R
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Design Principles of Data Analysis
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Design Principles of Data Analysis
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The 'Why' Behind Including 'Y' in Your Imputation Model
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Demystifying Invited Session Proposals
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Causal Quartet: When Statistics Alone Do Not Tell the Full Story
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Estimating Causal Effects: This be Madness, Yet There is Method in It
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A Visual Diagnostic Tool for Causal Inference
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STA 779 -- WFU Fall 2023
Causal Inference.
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STA 112 -- WFU Fall 2023
Introduction to Regression and Data Science.
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Visual Diagnostic Tool for Causal Inference
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Causal Inference in R
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Causal Inference in R
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Causal Inference is Not a Statistics Problem
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Are two wrong models better than one? Missing data style.
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When is complete case analysis unbiased?
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It’s just a linear model: neural networks edition
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Causal Quartets
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Causal Inference is Not Just a Statistics Problem
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Causal Inference Challenges that Go Beyond Statistics
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The Science of ChatGPT
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Kickstart your Career: How to Maximize Those Early Years
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Practical Principles for Data Analysis Design
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STA 363 -- WFU Spring 2023
Statistical learning.
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Design Principles for Data Analysis
The data revolution has led to an increased interest in the practice of data analysis.
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BEM 392 -- WFU Spring 2023
Seminar in Mathematical Business Analysis.
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Transparency in Public Health
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Randomized controlled trial: Quantifying the impact of disclosing uncertainty on adherence to hypothetical health recommendations
We conducted a randomized controlled trial to assess whether disclosing elements of uncertainty in an initial public health statement will change the likelihood that participants will accept new, different advice that arises as more evidence is uncovered.
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Statistical Myths and Misuse: Abandoning Outdated Statistical Practices
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Causal Inference in R
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Causal Inference in R
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Sensitivity Analyses for Unmeasured Confounders
This review expands on sensitivity analyses for unmeasured confounding techniques, demonstrating state-of-the-art methods as well as specifying which should be used under various scenarios, depending on the information about a potential unmeasured confoun…
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Migrating from Hugo to Quarto
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tipr: An R package for sensitivity analyses for unmeasured confounding
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ConTESSA: A Shiny Application to Assist with Evaluating the Impact of COVID-19 test-trace-isolate Programs
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Design Thinking: Empirical Evidence for Six Principles of Data Analysis
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STA 112 -- WFU Fall 2022
Statistical models.
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A Visual Diagnostic Tool for Causal Inference
Rosenbaum and Rubin (1983) suggested a visual representation, that can be used as a diagnostic tool, for examining whether the relationship between the confounders and the outcome are sufficiently controlled, or whether there is a more complex relationship
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Causal Inference in R
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Causal Inference in R
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Causal Inference in R
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Causal Inference in R
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Causal Inference in R
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Communicating During a Pandemic: What Worked, What Didn’t and What’s Next