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...

Thanks so much for spotlighting "The Data Analyst's Guide to Cause and Effect"! 📚🙌

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 👇

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!

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.

"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

"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

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

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 👇

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/

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/

Nice! Similar phenomenon to assurance (or marginal/average power), where a prior is placed over the effect and power is integrated over it to account for uncertainty, rather than conditioning on a single point estimate. Assurance is also always lower than power in practice.

Here's my current tentative and very much in progress outline. Comments of all kind much appreciated!

Imagine a short book (~200 p.) introducing Bayesian statistics in the context of clinical trials and drug development. Scope would be introductory -- sort of "your first short course on Bayes". But practical enough to be applied out of the box. What would you like to see covered in such a text? 👇

Yes, very cool! It's a class of methods often referred to as "Bayesian dynamic borrowing," and it's not well-known outside the clinical trial literature (and even there it's not very common). I give a brief intro to one particular approach here: theissbendixen.com/bayesian-dyn...

In the Fall I'll be teaching a new MA-level methods course entitled "Applied Statistical Evaluation of Development Projects". It will be 12 weeks, in R, and aimed around RCT evaluations. This is a draft outline. What am I missing? What seems redundant?

🚨 Blog post: When Using OLS Hurts 😩 I replicated a high-profile study on racial bias in tenure decisions and show the authors weakened their own findings by using OLS instead of ordered beta regression 🤯. Use ordered beta and live your best life 👍 #rstats www.robertkubinec.com/post/ord_bet...

This gives a flavor of the style: theissbendixen.com/bayesian-dyn... The scope (short, introductory, applied) is also similar to our forthcoming causal inference book: us2.sagepub.com/en-us/nam/th...

Imagine a short book (~200 p.) introducing Bayesian statistics in the context of clinical trials and drug development. Scope would be introductory -- sort of "your first short course on Bayes". But practical enough to be applied out of the box. What would you like to see covered in such a text? 👇

Yeah, Bayes is usually thought of as particularly useful with sparse data (because priors can do some of the work), but I think it's equally true that Bayes is useful when there's a lot of good data on e.g. a drug, because Bayes is very well-suited to exploit all that information in a principled way

Bayes is sometimes used at various stages in drug development. For instance in fancy meta-analysis: dmphillippo.github.io/multinma/ Borrowing in clinical trial analysis (worked example and some literature): theissbendixen.com/bayesian-dyn... Adaptive trials: hbiostat.org/doc/bayes/wh...

The Bayesian Workflow book is coming! statmodeling.stat.columbia.edu/2026/04/16/t...

Somewhat related to this, it turns out R.A. Fisher considered Bayes a frequentist? 🧐

Other applications of hierarchical models here for borrowing across different regions of the world. cdn.who.int/media/docs/d...

"Being Bayesian in a Frequentist World" New post on "Bayesian dynamic borrowing" in R 📚 Link 👇

Final manuscript submitted to the publisher! 📚