@maartenvsmeden.bsky.social
statistician • associate prof • team lead health data science and head methods research program at julius center • director ai methods lab, umc utrecht, netherlands • views and opinions my own
Happy to see this in print!
doi 10.1146/annurev-statistics-042324-123749
@maartenvsmeden.bsky.social @laurewynants.bsky.social @vanamsterdam.bsky.social and Ewout Steyerberg
Cluster analysis will always give you clusters
I have got the data
and I figured out exactly how to do my cluster analysis
all I need is a relevant question that my cluster analysis is going to answer
What if you combine open datasets with AI? Apparently, a 3 fold increase in low quality research papers, mass-produced by paper mills.
Interesting study in @jclinepi.bsky.social #academicsky #episky #medsky #Skystats
Thanks to @maartenvsmeden.bsky.social for initially posting this on Linkedin!
Our guidance regarding performance measures for medical AI models is finally out!
- Stop bashing AUROC, although it does not settle things
- Calibration and clinical utility are key
- Show risk distributions
- Classification statistics (e.g. F1) are improper
www.thelancet.com/journals/lan...
NEW PAPER
The use of explainable AI in healthcare evaluated using the well known Explain, Predict and Describe taxonomy by Galit Shmueli
link.springer.com/article/10.1...
🧐
this is one of my favourite observations about sample size calculations. (afaik first articulated by Miettinen in 1985)
Ha! I did not know I quoted Miettinen :). Thanks for the reference
For some research studies the optimal sample size should be estimated at 0
“Data available upon reasonable request” is academic language for you can get my data OVER MY DEAD BODY
I take version control very seriously
Manuscript_Final_Version_actualFINALcopy_version9b_USETHISONE.docx
Prediction models that are used to guide medical decisions are usually regulated under medical device regulation. This means, putting a calculator out there to promote the use your new prediction model is likely to break some rules.
The lasso works really well in particular settings and for particular purposes. If you are after high prediction performance alone and you have a rather large sample size, it can be an excellent choice indeed. But most analytical goals are not only about prediction
Kind reminder: data driven variable selection (e.g. forward/stepwise/univariable screening) makes things *worse* for most analytical goals
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Interpretable "AI" is just a distraction from safe and useful "AI"
This is right tho. Let’s therefore call them sensitivity positive predictive value curves bsky.app/profile/laur...
No.
I wonder who those people are who come here dying to know what GenAI has done with some prompt you put in
If you think AI is cool, wait until you learn about regression analysis
TL;DR: Explainable AI models often don't do a good job explaining. They can be very useful for description. We should be really careful when using Explainable AI in clinical decision making, and even when judging face validity of AI models
Excellently led by @alcarriero.bsky.social
NEW PREPRINT
Explainable AI refers to an extremely popular group of approaches that aim to open "black box" AI models. But what can we see when we open the black AI box? We use Galit Shmueli's framework (to describe, predict or explain) to evaluate
arxiv.org/abs/2508.05753
This is, however, not clever or safe writing, it is a bad collective habit that needs to stop. Not by avoiding references to causality but by clear referencing to it
pubmed.ncbi.nlm.nih.gov/37286459/
The healthcare literature is filled with "risk factors". This word combination makes research findings sound important by implying causality, while avoiding direct claims of having identified causal associations that are easily critiqued.
And taking this analogy one step further: it gives genuine phone repair shops a bad name
When forced to make a choice, my choice will be logistic regression model over linear probability model 103% of the time
Post just up: Is multiple imputation making up information?
tldr: no.
Includes a cheeky simulation study to demonstrate the point.
open.substack.com/pub/tpmorris...
