/explore
|
You are here 
|
www.rdatagen.net | ||
| | | | |
kgoldfeld.github.io
|
|
| | | | | ||
| | | | |
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... | |
| | | | |
aosmith.rbind.io
|
|
| | | | | I walk through an example of simulating data from a binomial generalized linear mixed model with a logit link and then exploring estimates of over/underdispersion. | |
| | | | |
swingleydev.com
|
|
| | | [AI summary] The author uses SQL, R, and Arduino programming to analyze historical temperature and snowfall data in Fairbanks, Alaska, to investigate trends in the timing and intensity of extreme cold and snow-free periods. | ||
