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hbiostat.org | ||
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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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fharrell.com
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
We've finally reached the end of the road. This is the fifth and last post in a series building up to a Bayesian proportional hazards model for analyzing a stepped-wedge cluster-randomized trial. If you are just joining in, you may want to start at the beginning. The model presented here integrates non-linear time trends and cluster-specific random effects-elements we've previously explored in isolation. There's nothing fundamentally new in this post; it brings everything together. Given that the groundwork has already been laid, I'll keep the commentary brief and focus on providing the code. |
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freerangestats.info
A few specific notes on technical issues relating to a previous post. On drawing network graphs with different coloured edges; modelling strategy; different specifications of models; and accessing UN SDG and gender inequality data. |
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