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 rpsychologist.com
Estimating treatment effects and ICCs from (G)LMMs on the observed scale using Bayes, Part 1: lognormal models | R Psychologist
| | fharrell.com
Ordinal Models for Paired Data - Statistical Thinking
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| | This article briefly discusses why the rank difference test is better than the Wilcoxon signed-rank test for paired data, then shows how to generalize the rank difference test using the proportional odds ordinal logistic semiparametric regression model. To make the regression model work for non-independent (paired) measurements, the robust cluster sandwich covariance estimator is used for the log odds ratio. Power and type I assertion \alpha probabilities are compared with the paired t-test for n=25. The ordinal model yields \alpha=0.05 under the null and has power that is virtually as good as the optimum paired t-test. For non-normal data the ordinal model power exceeds that of the parametric test.
| | www.civilytics.com
Mixed Effects Tutorial 2: Fun with merMod Objects - Civilytics
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| | Update: Since this post was released I have co-authored an R package to make some of the items in this post easier to do. This package is called merTools and is available on CRAN and on GitHub. To read more about it, read my new post hereand check out the packageon GitHub. Introduction First of [...]
| | aosmith.rbind.io
Simulate! Simulate! - Part 4: A binomial generalized linear mixed model
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| | 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.
| | www.fharrell.com
Borrowing Information Across Outcomes - Statistical Thinking
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| 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...