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Please enjoy some of my finest work.
In collaboration with my sister, Teesa D’Agostino, we bring you “I’m Just a Slope” a tribute to our love of statistics & The Lonely Island
Lucy D’Agostino McGowan
751 posts
Lucy D’Agostino McGowan
@LucyStats
Biostatistician • Assistant Prof @WakeForestStats • Postdoc @jhubiostat • PhD @vandy_biostat • SoMe Associate Editor @AmjEpi 🎙 @casualinfer • @WomeninStat
Joined September 2013
- 📣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!
- 🗣 Interested in conducting a sensitivity analysis for unmeasured confounding? It's easy! Here's a quick paper with several methods depending on your goals & what information you have available with real-data examples and {tipr} #rstats code link.springer.com/content/pdf/10…
- 🎙️ 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!)
M-Bias: Much Ado About Nothing? | Season 4 Episode 10 casualinfer.libsyn.com/m-bias-much-ad… - 📣 @malco_barrett, @travisgerke, and I have been working on some causal inference in #rstats projects (packages, workshops, and a new blog!) and have recently collected them all in a new website 👇
- 📈 I taught neural networks this week, so BEHOLD, an ode to @daniela_witten in the form of a shiny application: "it's just a linear model" neural nets edition lucy.shinyapps.io/neural-net-lin… #rstats
GIF - 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!
- New post on imputation 👀 but first, a {mice} question: I've generated a very simple missing data problem (c ➡️ x + missingness) when I use the defaults the model post imputation is super biased! Only if I specify to fit a simple regression model does the imputation work...why?
- 🗣 Y'ALL I just learned that you can iterate through code highlighting in Quarto slides using a |, for example, if I want to first show lines 1-5, then 6, then the whole thing I would add this option: #| code-line-numbers: "1-5|6|" so much copy-paste time saved!
- Precision medicine at the very least means leaving continuous variables continuous
GIF - Interested in Visual Diagnostic Tools for Causal Inference? My #JSM2023 slides along with a short blog post with code can be found here: 🎞 docs.google.com/presentation/d… ✍️ r-causal.github.io/r-causal-blog/…
- Once again reminded that for some reason when we say "fixed effects" we all mean different things (cc: @andrewheiss)! Tag yourself, I think I'm 5?
- My dear grandfather peacefully passed away last week — we already miss him so much, but I am so grateful to have this conversation with him from a few years ago recorded on @casualinfer @EpiEllie & I re-released it in his memory this week ♥️ casualinfer.libsyn.com/remembering-ra…
- One of the most common questions I get when talking about why you need to include the outcome in your imputation model is whether this is “double dipping” or “data leakage” — if this is you, what would convince you that this is not a concern?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!
