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Lucy D’Agostino McGowan Profile picture
Lucy D’Agostino McGowan
@LucyStats
Biostatistician • Assistant Prof @WakeForest • Postdoc @jhubiostat • PhD @vandy_biostat • SoMe Associate Editor @AmjEpi 🎙 @casualinfer • @WomeninStat
3 subscribers
Apr 18, 2024 • 12 tweets • 7 min read
Curious why statisticians recommend including the outcome in your imputation models? Check out our new paper in Statistical Methods in Medical Research! @SarahLotspeich, @StatStaci5, and I show with some simple mathematical derivations why this is really a requirement! Image There’s a bit of a twist, though! It turns out if you’re doing *deterministic* imputation you should NOT include the outcome in the imputation model, with stochastic imputation methods you must!

doi.org/10.1177/096228…
Deterministic vs stochastic imputation. With deterministic you plug predicted values in directly, with stochastic we add a bit of variability first
Dec 1, 2023 • 7 tweets • 4 min read
📣Our 🆕 paper Causal Inference is Not Just a Statistics Problem is out! @malco_barrett, @travisgerke, and I show that you can have 4 data sets with identical summary stats & visuals but very different data generating mechanisms-statistics alone can't tell you what to adjust for!

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Image 📦 We simulated a "Causal Quartet" (in the spirit of Ansombe's Quartet & others!) to demonstrate this phenomenon that you (or your students!) can play with in the {quartets} #rstats package



r-causal.github.io/quartets/
tandfonline.com/doi/full/10.10…
Apr 24, 2023 • 6 tweets • 4 min read
🎙️ On this weeks episode we talk about a “Causal Quartet” a set of four datasets generated under different mechanisms, all with the same statistical summaries (including visualizations!) but different true causal effects

(Plus a chat about M-bias!) 4 DAGS displaying a collide...Four scatter plots for each... Given a single dataset with 3 variables: exposure, outcome and covariate (z) how can statistics help you decide whether to adjust for z? It can’t! The correlation between z and the exposure in all 4 datasets is 0.7! Correct causal models and c...Coefficients for the exposu...
Dec 12, 2022 • 18 tweets • 5 min read
For this month's @AmJEpi tweetorial, I am going to walk through @jerudolph13, @eschisterman1, and @ashley_naimi's excellent simulation study comparing inverse probability weighting (IPW) and G-computation in survival analysis

doi.org/10.1093/aje/kw… First let's get some context! They built their simulation based on *real* trial data, the EAGeR Trial (designed to evaluate the relationship between low-dose aspirin use and several pregnancy outcomes). They used the distributions in this data to build the simulated variables