Bayesian reanalysis of ANDROMEDA-SHOCK

 https://benjamin-andrew.shinyapps.io/bayesian_trials/

Bayesian analysis of RCT data

https://discourse.datamethods.org/t/bayesian-analysis-of-rct-data/1997

ANDROMEDA-SHOCK (or, how to intepret HR 0.76, 95% CI 0.55-1.02, p=0.06)

https://discourse.datamethods.org/t/andromeda-shock-or-how-to-intepret-hr-0-76-95-ci-0-55-1-02-p-0-06/1349?fbclid=IwZXh0bgNhZW0BMQABHZl-ZyjEjp0uC3AroV4tHD4uxQ2apk8KRUD0QPmjpoWeHhuYIhwLt1Khog_aem_AWdNbe-zxt7yGCq0VdpfxAgfv4SMMR_sj6uZ_aVPGDevXxQ-vuVziIK1EaG1XpvJo1E

The statistical properties of RCTs and a proposal for shrinkage 21

https://onlinelibrary.wiley.com/doi/full/10.1002/sim.9173

David Spiegelhalter’s Gullible Skeptic, and a Bayesian “Hard-Nosed Skeptic” Reanalysis of the ANDROMEDA-SHOCK Trial

https://www.bayesianspectacles.org/david-spiegelhalters-gullible-skeptic-and-a-bayesian-hard-nosed-skeptic-reanalysis-of-the-andromeda-shock-trial/?fbclid=IwZXh0bgNhZW0BMQABHYL4q4fmISoKBdOdG63AYYpW7Ctf1r4nDhSdM58APNPMb9_kS3rN1Em15Q_aem_AWcOKXB3novlr9AAbiAJPv2HqEKRk-Fb0z7JqCD1AEzixfjd7vT1zf1UgSdUB0xAn_Y

On partial likelihood 24

https://scholar.google.com.tw/scholar?as_ylo=2024&hl=zh-TW&as_sdt=2005&sciodt=0,5&cites=183447912062803795&scipsc=#d=gs_qabs&t=1714004979406&u=%23p%3DcNF7px17NdMJ

Review of calculation of conditional power, predictive power and probability of success in clinical trials with continuous, binary and time-to-event endpoints 23

https://link.springer.com/article/10.1007/s10742-023-00302-5

https://ppos.shinyapps.io/public/

https://cran.r-project.org/web/packages/LongCART/index.html

Andromeda and ‘appalling science’: a response to Hardwicke and Ioannidis

https://medium.com/wintoncentre/andromeda-and-appalling-science-a-response-to-hardwicke-and-ioannidis-a79458efdba1

Effects of a Resuscitation Strategy Targeting Peripheral Perfusion Status versus Serum Lactate Levels among Patients with Septic Shock. A Bayesian Reanalysis of the ANDROMEDA-SHOCK Trial 2019

https://www.atsjournals.org/doi/10.1164/rccm.201905-0968OC

R: https://www.atsjournals.org/doi/suppl/10.1164/rccm.201905-0968OC/suppl_file/zampieri_data_supplement.pdf

Shiny:https://benjamin-andrew.shinyapps.io/andromeda_shock_bayesian/?fbclid=IwAR1qHXH4TdjT6h1APC0IS07tXcmRO-36UKvSMb_h8NRzH82TLw8zXXYqToo&mibextid=Zxz2cZ

1) an optimistic prior, which was based on the hypothesis that the log of the odds ratio (log[OR]) would be normally distributed with a mean of −0.4 and an SD of 0.4 (i.e., log[OR] ∼ N[−0.4, 0.4], corresponding to a median odds ratio [OR] of 0.67 with a 95% credible interval of 0.31–1.45); 2) a neutral prior in which the log[OR] of mean of 0 and an SD of 0.5, corresponding to an OR of 1 (95% credible interval, 0.37–2.75); 3) a null prior, in which all effect sizes were equally plausible; and 4) a pessimistic prior, prob 0.6 in which the log[OR] had a mean of 0.4 and an SD of 0.4, corresponding to an OR of 1.48 (95% credible interval, 0.68–3.26). 

The optimistic prior was designed to be more conservative than the OR used for power calculation in the main ANDROMEDA-SHOCK paper (which was based on an OR of 0.52 in favor of peripheral perfusion–targeted therapy); under this prior there was a probability of harm (i.e, OR > 1.0) of 15%. The pessimistic prior was designed to represent the same strength of belief as the optimistic prior but about harm.

https://iupbsapps.shinyapps.io/KruschkeFreqAndBayesApp/

The Importance of Prior Sensitivity Analysis in Bayesian Statistics

https://ucmquantpsych.shinyapps.io/sensitivityanalysis/


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