Discover and read the best of Twitter Threads about #epiellie

Most recents (5)

Happy New Year! To celebrate 2020, how about an #EpiEllie #tweetorial?

Today, let’s talk about e-values and how to interpret them!
E-values were first developed by Tyler Vanderweele and Peng Ding, and is a type of threshold-based quantitative bias analysis.

The original paper introducing the e-value is here:…
But today, I want to talk about a paper from the March 2019 issue of @AmJEpi which calculated e-values for 100 recent papers in each of #nutritionalepi and #environmentalepi, by @l_trinquart, @alerlinger, @JulieMOPetersen, @ProfMattFox, and @sandrogalea…
Read 21 tweets
Hello #epitwitter! Time for an @epiellie @AmJEpi tweetorial.

Today’s topic is the Target Trial Framework for #causalinference and how to apply it to improving observational studies.

So, what is the #targettrial framework?

Well it’s not a new method! Instead think of it as pedagogical device that provides a structured way to build your research question and study design for observational studies and minimizes the potential for bias.
What does that mean?

To design an observational study, we first think about what the ideal hypothetical randomized trial (target trial) is that would let us answer our research question.

Then, we try to match our observational study as closely as possible to that trial design.
Read 20 tweets
The Most Read article in @AmJEpi is a simple, but important, 2013 paper on gun ownership & suicide death in the US.

Critics argue you can’t make cause & effect conclusions based on ecological studies, but is that always true?

Let’s discuss! #epiellie…
So let’s start with some background. What is an ecological study?

It may sound like it’s about the environment, but that’s not really true.

In epidemiology, an ecological study is one where all data about groups not individuals.
What does that mean?

For example, we might know how much chocolate per capita is eaten in countries around the world and we might also know how many Nobel Prize winners each country has had.…
Read 25 tweets
It’s time for some #cartoonepi & #epiquiz fun!

Today, let’s talk about Difference-in-Difference analyses and how to use them to estimate the impact of policy changes!

Our example paper is from our May issue by @DrRitaHamad & colleagues.

I love this paper by @DrRitaHamad which tries to answer the question: did updating the allowed contents of the WIC package to include healthier options actually impact diet & nutrition during pregnancy?

First, some background for those of you not familiar with the WIC program.

WIC stands for “Special Supplemental Nutrition Program for Women, Infants, and Children”, and provides vouchers for specific food combos (‘packets’) for low-income pregnant women & kids <5yo in the US.
Read 22 tweets
Happy Tuesday, #epitwitter! There’s been a lot of discussion #onhere about #causalinference lately, but that’s not all that epi is about.

For this week’s #epichat with @epiellie, let’s talk about descriptive epi!

To kick things off, an #epiquiz:

How often do you do descriptive epidemiology in your work or research?

(click to see poll options)
Next question — this one’s for discussion so share your thoughts!

What differentiates descriptive epi from causal inference epi?

My take: descriptive epi is about who, what, where, & when; causal inference epi is about why, how, and what would happen if we changed something.
Read 11 tweets

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