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Reposted by Mathias Weis Damkjær

Rafe Meager (they/them) @economeager.bsky.social · 11/03/2026

Andy going thru the epstein files taking multiple killshots including at his own longtime coauthors............ incredible. posting in case you (like me) missed it statmodeling.stat.columbia.edu/2026/01/31/f...

statmodeling.stat.columbia.edu

From the Mixed-Up Files of Jeffrey E. Epstein | Statistical Modeling, Causal Inference, and Social Science

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Reposted by Mathias Weis Damkjær

Aki Vehtari @avehtari.bsky.social · 26/01/2026

Bayesian Workflow by Andrew Gelman, Aki Vehtari, @rmcelreath.bsky.social with @danpsimpson.bsky.social, @charlesm993.bsky.social, @yulingy.bsky.social, Lauren Kennedy, Jonah Gabry, @paulbuerkner.com, @modrakm.bsky.social, @vianeylb.bsky.social (in production, estimated copy-editing time 6 weeks)

**Part 1: From Bayesian inference to Bayesian workflow**

1. Bayesian theory and Bayesian practice
2. Statistical modeling and workflow
3. Computational tools
4. Introduction to workflow: Modeling performance on a multiple choice exam

**Part 2: Statistical workflow**

5. Building statistical models
6. Using simulations to capture uncertainty
7. Prediction, generalization, and causal inference
8. Visualizing and checking fitted models
9. Comparing and improving models
10. Statistical inference and scientific inference

**Part 3: Computational workflow**

11. Fitting statistical models
12. Diagnosing and fixing problems with fitting
13. Approximate algorithms and approximate models
14. Simulation-based calibration checking
15. Statistical modeling as software development

**4. Case studies**

16. Coding a series of models: Simulated data of movie ratings
17. Prior specification for regression models: Reanalysis of a sleep study
18. Predictive model checking and comparison: Clinical trial
19. Building up to a hierarchical model: Coronavirus testing
20. Using a fitted model for decision analysis: Mixture model for time series competition
21. Posterior predictive checking: Stochastic learning in dogs
22. Incremental development and testing: Black cat adoptions
23. Debugging a model: World Cup football
24. Leave-one-out cross validation model checking and comparison: Roaches
25. Model building and expansion: Golf putting
26. Model building with latent variables: Markov models for animal movement
27. Model building: Time-series decomposition for birthdays
28. Models for regression coefficients and variable selection: Student grades
29. Sampling problems with latent variables: No vehicles in the park
30. Challenge of multimodality: Differential equation for planetary motion
31. Simulation-based calibration checking in model development workflow

**Appendices**

A. Statistical and computational workflow for Bayesians and non-Bayesians
B. How to get the most out of Bayesian Data Analysis

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Stephen Senn @stephensenn.bsky.social · 24/11/2025

"we probably do not need to worry about the fact that the actual effect of one treatment rather than the other is not the same for all patients. Quite limited knowledge about an average improvement is the best that we can do" John Tukey, Controlled Clinical Trials, 1993 p282

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Marion Campbell @marionkcampbell.bsky.social · 18/08/2025

There has been a lot of debate recently about the promise of real world data - the routine (observational) data collected on patients eg treatments received, clinical outcomes etc – for estimating treatment effects. But can they deliver? 1/9 #MethodologyMonday #123

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Bada Yang @yangbd.bsky.social · 13/06/2025

❓ In people with HIV, is using two different rapid tests together ('parallel testing') to diagnose TB more accurate than using only one? An 'incremental' accuracy question: few Cochrane Reviews have yet addressed such questions. Adult review: shorturl.at/r9CUG Child review: shorturl.at/nSosK

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Wolfgang Viechtbauer @wviechtb.bsky.social · 06/03/2025

The Evidence Synthesis and Meta-Analysis in R Conference (ESMARConf) is back! It will be held June 11th to the 13th, 2025: esmarconf.org/2025/ Recordings of the talks and workshops from previous years can be found here: esmarconf.org/recordings/ #ESMARConf #MetaAnalysis #EvidenceSynthesis #RStats

esmarconf.org

2025 - ESMARConf

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Reposted by Mathias Weis Damkjær

Anders Huitfeldt @andershuitfeldt.net · 26/02/2025

After two years of trying to avoid this discussion, I just necroed *that thread* on datamethods (discourse.datamethods.org/t/should-one...) in order to share an excellent preprint by philosopher Veli-Pekka Parkkinen (philsci-archive.pitt.edu/24785/1/efme...)

discourse.datamethods.org

Should one derive risk difference from the odds ratio?

I urge all readers of this thread to read the excellent new preprint from philosopher of science Veli-Pekka Parkkinen, “Choice of effect measure, extrapolation, and decision-making in patient care an...

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Darren Dahly @statsepi.bsky.social · 09/01/2025

Two angels discussing the basics of treatment effects. (ICYMI) statsepi.substack.com/p/a-conversa...

statsepi.substack.com

A conversation on treatment effects

The trial statistician and the clinical investigator took a step back to admire their creation.

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Jack Wilkinson @jdwilko.bsky.social · 19/12/2024

Nice write up of our study applying potential trustworthiness checks to RCTs in 50 Cochrane Reviews in Nature by @richvn.bsky.social : www.nature.com/articles/d41...

nature.com

Giant study finds untrustworthy trials pollute gold-standard medical reviews

Two-year collaboration aims to create tools to help counter the tide of flawed research.

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