Nice example to explore type M and S errors If we assume true effect is HR = 0.7: - 11% chance that the estimate is in the wrong direction - Magnitude of the effect is exaggerated by a factor of 5.2 If HR = 0.5 - S-type error= 1.5% - M-type= exaggerated by a factor of 2.8 code: shorturl.at/Ba8nG

Very interesting distributions from a RCT of antibiotic. Bimodal peaking at 0 and ~7, while also truncated at 0 What distribution would fit this best?

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@gscollins.bsky.social could you please send me a screenshot of your letter? thanks jamanetwork.com/journals/jam...

COULD EVERYONE PLEASE STOP USING RANDOM EFFECTS META-ANALYSIS WHEN COMBINING 3 OR 4 TRIALS? AND COULD REVIEWERS STOP DEMANDING IT?

Couldn't hold myself, had to fit a #Bayesian cumulative ordinal model to this interesting data: Canadian intensivists and nephrologists don't seem to disagree much!

Added a statistical analysis tab to explore Bayesian partial pooling visualization (cc @rmcelreath.bsky.social). Added some model explanations too. Data viz heavily inspired by @benmoran.bsky.social's beautiful bayesfoRest package

Amazing work. I was particularly interested in visualizations for studies on dexmedetomidine for investigating dosage, timing, risk of bias. Vibe-coded this app: arthur-albuquerque.github.io/dexmedetomid... Example visualization attached. code: github.com/arthur-albuq... I'd love some feedback