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austinrochford.com | ||
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www.fharrell.com
In randomized clinical trials, power can be greatly increased and sample size reduced by using an ordinal outcome instead of a binary one. The proportional odds model is the most popular model for analyzing ordinal outcomes, and it borrows treatment effect information across outcome levels to obtain a single overall treatment effect as an odds ratio. When deaths can occur, it is logical to have death as one of the ordinal categories. Consumers of the results frequently seek evidence of a mortality reduction even though they were not willing to fund a study large enough to be able to detect this with decent power. The same goes when assessing whether there is an increase in mortality, indicating a severe safety problem for the new treatment. The partial propo... |
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www.karsdorp.io
I'm a researcher in Computational Humanities and Cultural Evolution at Amsterdam's [Meertens Institute](https://meertens.knaw.nl/index.php/en/), affiliated with the Royal Netherlands Academy of Arts and Sciences. I study aspects of cultural change and experiment with methods to quantify cultural diversity. A significant aspect of my recent work is understanding and accounting for biases in these quantifications. I like to use computational models from fields such as Machine Learning, Cultural Evolution, and Ecology to aid these investigations. Beyond research, I have a passion for teaching computer programming, especially within the Humanities context. Together with [Mike Kestemont](http://mikekestemont.github.io/) and [Allen Riddell](https://www.ariddell.or... |
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twiecki.io
[AI summary] This technical blog post explains the advantages of hierarchical Bayesian modeling over non-hierarchical approaches using a case study of predicting radon levels across different US counties with the PyMC3 library. |
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austinrochford.com
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