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articles.foletta.org | ||
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blog.foletta.net
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fharrell.com
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| | | | | Historical data (HD) are being used increasingly in Bayesian analyses when it is difficult to randomize enough patients to study effectiveness of a treatment. Such analyses summarize observational studies' posterior effectiveness distribution (for two-arm HD) or standard-of-care outcome distribution (for one-arm HD) then turn that into a prior distribution for an RCT. The prior distribution is then flattened somewhat to discount the HD. Since Bayesian modeling makes it easy to fit multiple models at once... | |
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www.rdatagen.net
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| | | | | I've been curious to see how helpful ChatGPT can be for implementing relatively complicated models in R. About two years ago, I described a model for estimating a treatment effect in a cluster-randomized stepped wedge trial. We used a generalized additive model (GAM) with site-specific splines to account for general time trends, implemented using the mgcv package. I've been interested in exploring a Bayesian version of this model, but hadn't found the time to try - until I happened to pose this simple question to ChatGPT: | |
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www.huber.embl.de
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| | | If you are a biologist and want to get the best out of the powerful methods of modern computational statistics, this is your book. | ||
