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www.rdatagen.net | ||
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kgoldfeld.github.io
Simulates data sets in order to explore modeling techniques or better understand data generating processes. The user specifies a set of relationships between covariates, and generates data based on these specifications. The final data sets can represent data from randomized control trials, repeated measure (longitudinal) designs, and cluster randomized trials. Missingness can be generated using various mechanisms (MCAR, MAR, NMAR). |
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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.cedricscherer.com
A step-by-step tutorial explaining how my visualizations have evolved from a typical basic ggplot. Here, I transform a basic boxplot into a compelling and self-explanatory combination of a jittered dot strip plot and a lollipop plot. |
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fronkonstin.com
Experiment, be curious: though interfering friends may frown, get furious at each attempt to hold you down (Tony Bennett, Experiment) Instructions: Take a pencil and measure it Take a piece of paper and draw parallel lines on it (you can use the pencil, of course); separation between lines should double the length of the pencil Toss the pencil |
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