Regarding metric- vs content-based science, I see 3 general positions: - Idealists: contents matter 100%; highly needed but most disappear (frustration, don't get promotions) - Pragmatists: focus on content but use metrics to survive - Conformists: metrics matter 100%

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

Doug Altman was an internationally renowned statistician who served as The BMJ’s chief statistical adviser. Read about life and work that made this statistician a "citation millionaire" #BMJChristmas www.bmj.com/content/391/...

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...

Expertise is having fucked up in enough different ways that you become able to anticipate it.

In our latest work, we show that risk estimates for patients are HUGELY uncertain due to model, data, and population uncertainty. Even for well performing models (c statistic, calibration, utility) based on large N. @laure_wynants @ESteyerberg @lasaibarrenada.bsky.social arxiv.org/abs/2506.17141

Universities love open science Small print: unless money is involved

Huge variability documented in how publishers respond when informed about a problematic body of work by a research group. www.jclinepi.com/article/S089... #publishers #retractions

Multiple Imputation of Missing Covariates When Using the Fine–Gray Model. Edouard F. Bonneville, Jan Beyersmann, Ruth H. Keogh, Jonathan W. Bartlett, Tim P. Morris, Nicola Polverelli, Liesbeth C. de Wreede, Hein Putter. Statistics in Medicine. onlinelibrary.wiley.com/doi/10.1002/...

Seconded. Any time someone uses this term, make sure they explain exactly what they mean. If they can't, they are obviously trying to bullshit you.

“Volume is a bad driver,” [Sir Mark Walport] said. “The incentive should be quality, not quantity. It’s about re-engineering the system in a way that encourages good research from beginning to end.” www.theguardian.com/science/2025...

What is common knowledge in your field, but shocks outsiders? Data isn't objective and researchers have innumerable ways to put their thumbs on the scale. Many don't understand statistics well enough to realize they're doing it.

**New Lancet DH paper** "Importance of sample size on the quality & utility of AI-based prediction models for healthcare" - for broad audience - explains why inadequate SS harms #AI model training, evaluation & performance - pushback to claims SS irrelevant to AI research 👇 tinyurl.com/yrje52fn

I recently had a paper in a journal where the fee for open access was lower than the fee for closed access. Is that common? @grahamkendall.bsky.social

Oh, there's an English version as well! DW reports about misconduct at Max Planck Institutes: www.youtube.com/watch?v=n5nE...

PROBAST+AI is out! www.bmj.com/content/388/...

I will be presenting our recent work on individual risk estimation uncertainty at ENAR in New Orleans. Come say hi! Work with @benvancalster.bsky.social @laurewynants.bsky.social #DoranneThomassen #EwoutSteyerberg

Quousque tandem? @kuleuvenuniversity.bsky.social @fwovlaanderen.bsky.social A lot of publications in MDPI journals also in Flanders, despite it being listed on predatoryjournals.org.

🚨 Preprint! We combine our recent open dataset of #APC prices with the article counts per journal-year from #OpenAlex to estimate how much the academic community has paid in APCs over the last 5 years. A. $8.349 billion ($8.968 B in 2023 USD) $2.5B in 2023 alone. arxiv.org/abs/2407.16551 #metasci

my life would radically improve if i weren’t subjected to microsoft products..,. like my days would feel a 1000 x better

NEW PREPRINT 📊: We propose 3 methods to obtain flexible calibration plots while accounting for clustering: 1. Clustered Group Calibration (CG-C) 2. Two-Stage Meta-Analysis Calibration (2MA-C) 3. Mixed Model Calibration (MIX-C) Ready-to-use R code included!

We tried to look at ways to obtain flexible calibration plots in clustered (e.g. multicenter) validation studies. Work with @lasaibarrenada.bsky.social @laurewynants.bsky.social @bavodccampo.bsky.social arxiv.org/abs/2503.08389

It is a good day today, I got the reviewer comment "please consult a statistician" again.

'Onze universiteit roept op tot evidence-based beleid, maar zelf volgt de rector zijn buikgevoel' www.veto.be/onderzoek/on...

Bad. But how bad? I once asked myself, while finding fakes in the Scientific Report @natureportfolio.bsky.social. So I decide to "do my our research" - I took 100 articles in a row from Physical sciences that contains diffraction. Result - 15 fakes out 100. Details in the 🧵 #ResearchIntegrity 1/x

NEW PREPRINT A detailed overview of 32 popular predictive performance metrics for prediction models arxiv.org/abs/2412.10288

New work in preprint! "Performance evaluation of predictive AI models to support medical decisions: Overview and guidance". Under the wings of the STRATOS initiative. But @maartenvsmeden.bsky.social said it better already 😜 arxiv.org/abs/2412.10288

Is this something everybody knows already but me? The joy of EHR! www.youtube.com/watch?v=xB_t...

FFS www.sciencedirect.com/science/arti...

Covariate adjustment = AI digital twins 😆 Such dishonesty Good luck regulators and HTAs. I hope you are able to hold the line and insist on evidence for all the claims in this advert. www.insideprecisionmedicine.com/topics/infor...

I'm always getting stuck when I try to sharply define (the differences between) biases like information bias, observer bias, detection bias, and ascertainment bias. I read quite some contradicting things. How should I see this?

I will not be completing this survey and would encourage everyone else to do likewise. These opinions are converted into so called measurements of university performance in the THE World University Rankings - a ridiculous and damaging exercise for the sector.