An introduction to precisely and ggdag: tools for modern methods in R

Abstract

Modern epidemiology gives us insight into study planning and causal inference, but the success of these approaches require friendly and accessible software. I will discuss two R packages for modern methods in study design and causal inference: precisely and ggdag. precisely is a study planning tool to calculate sample size based on precision rather than power. Calculating sample size based on precision focuses on the width of the confidence interval instead of statistical significance. precisely is a fast and flexible R implementation of the work by Rothman and Greenland on this subject, including a Shiny web app for calculating sample size. ggdag is a toolkit for working with causal directed acyclic graphs (DAGs), a central tool in causal inference. DAGs help identify many types of bias, like confounding, selection bias, and measurement error, as well as tell us how to correct for it. ggdag makes it easy to create, analyze, and plot DAGs in ggplot2.

Date
Sep 4, 2019 4:00 PM
Event
Berlin Epidemiological Methods Colloquium
Location
Berlin, Germany
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Malcolm Barrett
PhD Student in Epidemiology

I am an R developer and a PhD student in Epidemiology at the University of Southern California. My work in public health has spanned on-ground clinical education and research for clinical and cohort studies. Previously, I was an intern at RStudio, and I served two years in AmeriCorps at federally-qualified health centers in Michigan and New York City.

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