Zad Rafi (Sir Panda)'s Avatar

Zad Rafi (Sir Panda)

@lesslikely.com

Agnostic statistician (frequentist, bayesian, likelihoodist, fiducial) | Posts about statistics in medicine at http://lesslikely.com | | #StatsTwitter • #EpiTwitter • #RStats

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14.10.2023
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Latest posts by Zad Rafi (Sir Panda) @lesslikely.com

And in case people have not read it - this is worth a read www.cell.com/neuron/fullt... .. by @deevybee.bsky.social and others

24.02.2026 15:10 👍 28 🔁 7 💬 1 📌 1

I feel like someone’s gotta say something, so I guess it’s going to be me. I’m 100% certain that p-values are useful.

01.02.2026 11:09 👍 64 🔁 3 💬 17 📌 0

Excited to read this !!

23.01.2026 04:46 👍 0 🔁 0 💬 0 📌 0
Preview
A Fully‐Integrated Bayesian Approach for the Imputation and Analysis of Derived Outcome Variables With Missingness Derived variables are variables that are constructed from one or more source variables through established mathematical operations or algorithms. For example, body mass index (BMI) is a derived varia....

New paper:
‘A fully-integrated Bayesian approach for the imputation and analysis of derived outcome variables with missingness’
Harlan Campbell, me and Paul Gustafson
onlinelibrary.wiley.com/doi/full/10....

22.01.2026 21:35 👍 15 🔁 4 💬 2 📌 0

Do people still use blogdown/hugo or has everyone moved onto Quarto /substack?

06.01.2026 08:35 👍 2 🔁 2 💬 1 📌 0

Do people still use blogdown/hugo or has everyone moved onto Quarto /substack?

06.01.2026 08:35 👍 2 🔁 2 💬 1 📌 0
Post image

S-Values are much more interpretable than P-values, yet adoption seems near impossible. I wonder what it would take to make the leap? #statssky #episky #rstats #statistics

10.10.2025 15:50 👍 40 🔁 16 💬 7 📌 4

Everyone should use {marginaleffects} because it includes s-values

25.11.2025 15:41 👍 43 🔁 4 💬 9 📌 0

@tjmahr.com might actually make me start using this platform 😂

25.11.2025 19:01 👍 1 🔁 0 💬 0 📌 0

When planning for drop outs: If you need n patients and expect some proportion x to drop out, you don't inflate n by 1.x but rather divide by 1-x.

Example: n = 200 and expected 20% drop out

200 * 1.20 = 240 (incorrect, as 80% of 240 = 192)

200 / 0.8 = 250 (correct, as 80% of 250 = 200)

09.06.2025 08:50 👍 52 🔁 7 💬 3 📌 3

People can’t hand out randomized envelopes properly and accidentally randomize entire villages instead of people and were supposed to believe in a large randomized study with no other issues

06.06.2025 13:05 👍 1 🔁 0 💬 1 📌 0

He was right, and with large language AI models you don’t even need to conduct studies

06.06.2025 13:00 👍 1 🔁 0 💬 1 📌 1

In that case, with a large sample size and other biases, you have a narrow interval around the wrong value, making you overconfident in range that could be off to make a practical difference

06.06.2025 12:53 👍 2 🔁 0 💬 1 📌 0

Plus, the idea of a very large trial with a precise estimate and no other biases is fairy tale. If people accidentally randomize entire villages or can’t hand out envelopes right, imagine how perfectly they’re conducting all the other protocols of the study

06.06.2025 12:51 👍 0 🔁 0 💬 0 📌 0

It’s not a bad article but there are some misunderstanding of how certain procedures work, which makes it frustrating when seeing an authority of EBM creating occasional blanket rules of thumb for what’s desirable

06.06.2025 12:48 👍 2 🔁 0 💬 1 📌 0

If you wanna balance prognostic factors so badly, why not measure them beforehand and divide the subjects into two groups to achieve perfect balance and the sought after “no differences between groups”?

06.06.2025 12:42 👍 1 🔁 0 💬 2 📌 0

Naturally the only thing to do after seeing this is to suggest that you actually test whether randomization has succeeded via a statistical test for balance, and if it’s not, just rerandomize forever until you achieve perfect balance and the holy p =1

06.06.2025 12:39 👍 5 🔁 1 💬 1 📌 0
Preview
The Use of Historical Controls in Clinical Trials, With Dr Althouse JAMAevidence JAMA Guide to Statistics and Methods · Episode

"Trials" with historical controls - the worst of all worlds.

open.spotify.com/episode/08PC...

01.06.2025 09:59 👍 22 🔁 3 💬 6 📌 0
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RIP Edward Leamer. Specification Searches was ahead of its time

02.03.2025 18:51 👍 1 🔁 1 💬 0 📌 0

Nice summary of our recent paper on SMOTE w/ @alcarriero.bsky.social @benvancalster.bsky.social

06.03.2025 13:40 👍 16 🔁 2 💬 0 📌 1
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RIP Edward Leamer. Specification Searches was ahead of its time

02.03.2025 18:51 👍 1 🔁 1 💬 0 📌 0
Screenshot of http://jmlr.org/papers/v26/23-1317.html

Screenshot of http://jmlr.org/papers/v26/23-1317.html

My work on network regression and mediation in latent space models is now published at JMLR!

jmlr.org/papers/v26/2...

10.02.2025 23:30 👍 25 🔁 6 💬 2 📌 0