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Theiss Bendixen

@theissbendixen.bsky.social
138 followers 72 following 90 posts

Data science & Bayes at Novo Nordisk | Author of "The Data Analyst's Guide to Cause and Effect" (theissbendixen.com/dag-book) | Writing a book on Bayes in drug development | Board member, giveffektivt.dk www.theissbendixen.com

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Theiss Bendixen @theissbendixen.bsky.social · 16/09/2026
Agree, the paper seems fairly balanced. Of course an informative priors requires justification and sensitivity analysis. But I think that principle should apply in the other direction too, such that a frequentist analysis is required to justify the relevant data that it ignores.
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Theiss Bendixen @theissbendixen.bsky.social · 10/09/2026
"Descriptive statistics also require causal inference" So true! In the Data Analyst's Guide, we illustrate it with a fun example: a registry study on spirituality among Danes 👇 theissbendixen.com/dag-book/
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Theiss Bendixen @theissbendixen.bsky.social · 09/09/2026
Cool, thanks! Is there a vignette somewhere on the marginalisation approach to group-level parameters?
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Theiss Bendixen @theissbendixen.bsky.social · 04/09/2026
Big congrats -- and big thanks for a great piece of software! Can you say a little more on how to get the "unconditional" standard error out? Tried to look at a few case studies but am probably overlooking something - thanks again!
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Theiss Bendixen @theissbendixen.bsky.social · 05/06/2026
New blog post! 🚨 "From Bucher to Bayes: A Brief Introduction to Bayesian Model-Based Network Meta-Analysis for Indirect Treatment Comparisons using R" theissbendixen.com/mbnma/
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Theiss Bendixen @theissbendixen.bsky.social · 06/06/2026
It's alive! 🎉 𝗧𝗵𝗲 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁'𝘀 𝗚𝘂𝗶𝗱𝗲 𝘁𝗼 𝗖𝗮𝘂𝘀𝗲 𝗮𝗻𝗱 𝗘𝗳𝗳𝗲𝗰𝘁 is out -- an introduction to causal inference in practice. The first two chapters are available for free here: theissbendixen.com/dag-book/ More below 👇
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Andrew Gelman et al. @statmodeling.bsky.social · 25/08/2026
Bayesian Workflow free pdf! statmodeling.stat.columbia.edu/2026/08/25/b...
statmodeling.stat.columbia.edu
Bayesian Workflow free pdf! | Statistical Modeling, Causal Inference, and Social Science
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Theiss Bendixen @theissbendixen.bsky.social · 18/08/2026
Hi Frank, I sent you an email re. this, at fh@fharrell.com 😊 Best wishes
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Theiss Bendixen @theissbendixen.bsky.social · 28/07/2026
Nice, thanks! Agree, robustifying a prior is often useful. There are also approaches like "Bayesian dynamic borrowing" that increase borrowing when data and prior align and reduce borrowing in case of conflict - quick intro here: theissbendixen.com/bayesian-dyn...
theissbendixen.com
Being Bayesian in a Frequentist World
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Theiss Bendixen @theissbendixen.bsky.social · 23/07/2026
McElreath's Statistical Rethinking (2nd ed) has Stan blocks explained bit by bit in certain chapters? 😊 Also the model syntax in the {rethinking} package can work as a scaffold towards learning Stan. @rmcelreath.bsky.social
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Theiss Bendixen @theissbendixen.bsky.social · 15/07/2026
Some of the best statisticians I know are not ✨ real statisticians ✨
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Theiss Bendixen @theissbendixen.bsky.social · 13/07/2026
Right! Our efforts were in fact guided by a model of brain size [1], which was originally motivated by primates, but also makes predictions for an asocial path to big brains that seems to characterise the cephs [2] [1] journals.plos.org/ploscompbiol... [2] inference-review.com/letter/the-e...
inference-review.com
The Evolution of Big Brains | The Evolution of Big Brains | Inference
The cultural brain hypothesis predicts two main paths to intelligence and large brains in animals: a social learning path taken by humans at one end, and an asocial learning path taken by cephalopods ...
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Theiss Bendixen @theissbendixen.bsky.social · 13/07/2026
Right! Our efforts were in fact guided by a model of brain size [1], which was originally motivated by primates, but also makes predictions for an asocial path to big brains that seems to characterise the cephs [2] [1] journals.plos.org/ploscompbiol... [2] inference-review.com/letter/the-e...
inference-review.com
The Evolution of Big Brains | The Evolution of Big Brains | Inference
The cultural brain hypothesis predicts two main paths to intelligence and large brains in animals: a social learning path taken by humans at one end, and an asocial learning path taken by cephalopods ...
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Theiss Bendixen @theissbendixen.bsky.social · 06/07/2026
This arrived just in time for Danish summer weather (rain). A few chapters in and it does not dissappoint! Remarkable mix of theory and practice, so many good points to absorb -- hope it's widely read. Thanks @statmodeling.bsky.social, @avehtari.bsky.social, @rmcelreath.bsky.social, et al.!
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Theiss Bendixen @theissbendixen.bsky.social · 03/07/2026
New research! 📚 We built the largest database to date of cephalopod species - octopuses, squids and cuttlefish - and their brains, habitats and behaviors 🐙🧠📈 What did we find?👇 Press release: www.lse.ac.uk/news/ecologi... Paper: www.sciencedirect.com/science/arti...
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Theiss Bendixen @theissbendixen.bsky.social · 03/07/2026
And of course, all data and code are freely available -- check out the paper for details. www.sciencedirect.com/science/arti...
sciencedirect.com
Ecological not social factors explain brain size in cephalopods
Social factors have been argued to be the main selection pressure for the evolution of large brains and complex behavior, but many cephalopods live la…
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Theiss Bendixen @theissbendixen.bsky.social · 03/07/2026
Oh, and by the way, we have *much* more data from this project than we present here. So feel free to reach out for potential collaboration! 🙌 @michael.muthukrishna.com
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Theiss Bendixen @theissbendixen.bsky.social · 03/07/2026
In short, cephalopods are an evolutionary enigma and a promising animal group on which to evaluate prominent hypotheses on the evolutionary drivers of brain size. So that's what we set out to do!
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Theiss Bendixen @theissbendixen.bsky.social · 03/07/2026
Cephalopods are also mostly short-lived, asocial and cannibalistic, they exhibit little to no parental care or pair-bonding, and usually die shortly after first reproduction. This runs counter to theories of brain size and intelligence in large-brained vertebrates.
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Theiss Bendixen @theissbendixen.bsky.social · 03/07/2026
Cephalopods are phylogenetically very distant from animals usually considered “brainy,” branching off from the vertebrates over 500 million years ago. And yet, many cephalopods possess large and complex nervous systems and exhibit flexible behaviours.
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Theiss Bendixen @theissbendixen.bsky.social · 03/07/2026
In short: good ol' slow-cooked, sous vide science 🤌✨
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Theiss Bendixen @theissbendixen.bsky.social · 03/07/2026
For instance, we reviewed the ecology, behavior, life-history and physiology of all ceph species for which we have brain size data, and we also built a phylogeny of these species to allow for statistical modeling of phylogenetic relationships. Phylogeny pre-print: www.biorxiv.org/content/10.1...
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Theiss Bendixen @theissbendixen.bsky.social · 03/07/2026
First of all, this was a *huge*, multi-year (+7 years) team effort! My own research has taken a few turns since we began this, but our review, database and analyses are still a massive contribution to the field of cephalopod and comparative brain studies, in my humble opinion.
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Theiss Bendixen @theissbendixen.bsky.social · 03/07/2026
New research! 📚 We built the largest database to date of cephalopod species - octopuses, squids and cuttlefish - and their brains, habitats and behaviors 🐙🧠📈 What did we find?👇 Press release: www.lse.ac.uk/news/ecologi... Paper: www.sciencedirect.com/science/arti...
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Kristoffer Magnusson @rpsychologist.com · 25/06/2026
New interactive blog! "Why Adjusted Regression Coefficients Are Less Descriptive Than They Look" rpsychologist.com/descriptive-...
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Theiss Bendixen @theissbendixen.bsky.social · 26/06/2026
Brilliant read! I'd add that several of the big COVID trials took an explicit Bayesian approach, which facilitates interim looks at the data and allows a trial to stop if the treatment is clearly working (or not). @statberry.bsky.social gives a readable overview here: www.mdpi.com/2077-0383/14...
mdpi.com
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Theiss Bendixen @theissbendixen.bsky.social · 11/06/2026
Can't get a specific date but it'll roll out over the next few weeks/months -- apparently this is normal for these kinds of books (?)
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Theiss Bendixen @theissbendixen.bsky.social · 11/06/2026
Thanks so much for spotlighting "The Data Analyst's Guide to Cause and Effect"! 📚🙌
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Theiss Bendixen @theissbendixen.bsky.social · 09/06/2026
It seems to be delayed outside US. But maybe we can arrange a physical copy at AU through one of @bgpurzycki.bsky.social author copies? Then I'll owe Ben one 😀
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Theiss Bendixen @theissbendixen.bsky.social · 08/06/2026
Huh, good question, thanks! I'm sure it'll be available at some point (I actually thought it already was), but will check 👍
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Theiss Bendixen @theissbendixen.bsky.social · 07/06/2026
... and this 👇 bsky.app/profile/thei...
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Theiss Bendixen @theissbendixen.bsky.social · 07/06/2026
Related to this 👇 bsky.app/profile/thei...
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Theiss Bendixen @theissbendixen.bsky.social · 07/06/2026
There's also a bonus section on "non-centered" parameterisation 🤓
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Theiss Bendixen @theissbendixen.bsky.social · 07/06/2026
For fitting the MBNMA, I use @rmcelreath.bsky.social's rethinking package, where the syntax satisfyingly mirrors the formal model.
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Theiss Bendixen @theissbendixen.bsky.social · 07/06/2026
I give a brief introduction to Bayesian model-based network meta-analysis (MBNMA) to model studies on different dose levels of the same drug and show how it can be used to inform an indirect treatment comparison between competitive drugs that have not been studied in the same trial.
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Theiss Bendixen @theissbendixen.bsky.social · 07/06/2026
It’s common for meta-analyses in the clinical trial literature to lump together studies with different doses, follow-up times, or populations. But often we can do better than that and instead model the dependencies or discrepancies explicitly. One potential payoff is increased statistical precision.
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Theiss Bendixen @theissbendixen.bsky.social · 06/06/2026
Instead, we cut to the chase and emphasize a practical workflow using step-by-step explanations and real data examples in R. The companion website lives here theissbendixen.com/dag-book and holds: - All data and code used in the book - Free sample chapters - Bonus material!
theissbendixen.com
The Data Analyst's Guide to Cause and Effect
This is the companion website for The Data Analyst's Guide to Cause and Effect
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Theiss Bendixen @theissbendixen.bsky.social · 06/06/2026
It took us three years to write this thing. But the good news is you can read it in three days! We cover fairly advanced methods -- counterfactuals, g-computation, inverse probability of treatment weighting, poststratification, missing data imputation, etc. -- without dense formal notation.
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Theiss Bendixen @theissbendixen.bsky.social · 06/06/2026
"Strongly application-focused... an effective tool for getting data analysts into the world of causal inference and immediately into a workable project." -- Nick Huntington-Klein, @nickchk.com
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Theiss Bendixen @theissbendixen.bsky.social · 06/06/2026
"An excellent, comprehensive, yet accessible introduction to causal inference... an invaluable guide for analysts seeking to move beyond mere correlation." -- Julia Rohrer, @dingdingpeng.the100.ci
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Theiss Bendixen @theissbendixen.bsky.social · 06/06/2026
First, we're very lucky that some very impressive people have already said some very nice words about the book! "A clear and readable book with broad coverage of many ideas and methods in causal inference." -- Andrew Gelman, @statmodeling.bsky.social
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Theiss Bendixen @theissbendixen.bsky.social · 06/06/2026
Published with @sagepub.com More here: collegepublishing.sagepub.com/products/the...
collegepublishing.sagepub.com
The Data Analyst’s Guide to Cause and Effect
Understanding cause-and-effect relationships is essential for credible research and informed decision-making. The Data Analyst’s Guide to Cause and Effect offers a clear, practical ...
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Theiss Bendixen @theissbendixen.bsky.social · 06/06/2026
Instead, we cut to the chase and emphasize a practical workflow using step-by-step explanations and real data examples in R. The companion website lives here theissbendixen.com/dag-book and holds: - All data and code used in the book - Free sample chapters - Bonus material!
theissbendixen.com
The Data Analyst's Guide to Cause and Effect
This is the companion website for The Data Analyst's Guide to Cause and Effect
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Theiss Bendixen @theissbendixen.bsky.social · 06/06/2026
It took us three years to write this thing. But the good news is you can read it in three days! We cover fairly advanced methods -- counterfactuals, g-computation, inverse probability of treatment weighting, poststratification, missing data imputation, etc. -- without dense formal notation.
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Theiss Bendixen @theissbendixen.bsky.social · 06/06/2026
"Strongly application-focused... an effective tool for getting data analysts into the world of causal inference and immediately into a workable project." -- Nick Huntington-Klein, @nickchk.com
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Theiss Bendixen @theissbendixen.bsky.social · 06/06/2026
"An excellent, comprehensive, yet accessible introduction to causal inference... an invaluable guide for analysts seeking to move beyond mere correlation." -- Julia Rohrer, @dingdingpeng.the100.ci
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Theiss Bendixen @theissbendixen.bsky.social · 06/06/2026
First, we're very lucky that some very impressive people have already said some very nice words about the book! "A clear and readable book with broad coverage of many ideas and methods in causal inference." -- Andrew Gelman, @statmodeling.bsky.social
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Theiss Bendixen @theissbendixen.bsky.social · 06/06/2026
It's alive! 🎉 𝗧𝗵𝗲 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁'𝘀 𝗚𝘂𝗶𝗱𝗲 𝘁𝗼 𝗖𝗮𝘂𝘀𝗲 𝗮𝗻𝗱 𝗘𝗳𝗳𝗲𝗰𝘁 is out -- an introduction to causal inference in practice. The first two chapters are available for free here: theissbendixen.com/dag-book/ More below 👇
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Theiss Bendixen @theissbendixen.bsky.social · 05/06/2026
New blog post! 🚨 "From Bucher to Bayes: A Brief Introduction to Bayesian Model-Based Network Meta-Analysis for Indirect Treatment Comparisons using R" theissbendixen.com/mbnma/
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Theiss Bendixen @theissbendixen.bsky.social · 03/06/2026
Totally agree! We aspire to do this in The Data Analyst's Guide to Cause and Effect, obv. inspired by folks like @rmcelreath.bsky.social and @statmodeling.bsky.social theissbendixen.com/dag-book/
theissbendixen.com
The Data Analyst's Guide to Cause and Effect
This is the companion website for The Data Analyst's Guide to Cause and Effect
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