New paper! 😃 Can prediction models developed before the introduction of a new treatment, be used to estimate treatment effects of newly introduced treatments? We dive deeper into this approach and its necessary conditions link.springer.com/article/10.1...

Treatment effect estimation by comparing observed and predicted outcomes: conditions for valid inference and practical illustration - Diagnostic and Prognostic Research

Background One non-randomized approach to estimate the average effect of a newly introduced treatment is to compare observed outcomes under the new treatment with predicted outcomes under the standard...

link.springer.com

Plug-and-play use of tree-based methods: Consequences for clinical prediction modelling - Journal of Clinical Epidemiology www.jclinepi.com/article/S089...

New paper! 😃 We talk about using tree-based methods, such as Random Forests, for clinical prediction & what (not) to expect from them, with many illustrations. Let me know what you think! doi.org/10.1016/j.jc...

Still some spots available in our summer school on all things causal inference, 7-11 July in Utrecht! Discounts for those working in universities and non-profits, and affordable accommodation offered by @utrechtuniversity.bsky.social summer school!

Oisín Ryan@oisinryan.bsky.social · 2y ago

Interested in how to use non-experimental data to answer causal research questions? Mystified by DAGs and counterfactuals? Want to learn what Target Trial Emulation is all about? Sign up now for the 2nd edition of our summer school, 7-11 July in Utrecht, with @vanamsterdam.bsky.social & BPdeVries

Happy to share the first paper of my PhD is published☺️! In case you like to use class imbalance corrections, maybe it is interesting. Let me know what you think! onlinelibrary.wiley.com/doi/10.1002/... Many thanks to @maartenvsmeden.bsky.social, @benvancalster.bsky.social, Anne, Kim and Carl !!

This might be the first time after 10 years that boosted trees are not the best default choice when working with data in tables. Instead a pre-trained neural network is, the new TabPFN, as we just published in Nature 🎉

Some time ago I wrote a non-technical introduction to marginal effects, in case you don't know the underlying logic yet. The idea is to use your model as a prediction machine that can be queried to return answers to precisely the questions you're interested in. www.the100.ci/2022/05/27/%...

✨ Unleash your inner stats sparkle ✨ with this very non-technical introduction to marginal effects

Let’s admit right away that “marginal effects” doesn’t sound like the most sexy topic. I’m not saying that to further marginalize these poor effects. It’s just that statistics, in and of itself, barel...

the100.ci

Happy to share the first article of my PhD, which is now available as a pre-proof! We looked at methods used to adjust existing (AI/ML) clinical prediction models to new contexts, like different hospitals, clinical domains or to a specific individual. Curious to hear your thoughts!😃

Interested in how to use non-experimental data to answer causal research questions? Mystified by DAGs and counterfactuals? Want to learn what Target Trial Emulation is all about? Sign up now for the 2nd edition of our summer school, 7-11 July in Utrecht, with @vanamsterdam.bsky.social & BPdeVries