Introducing PowerLMM.js: Browser-based power analysis for longitudinal models with dropout
Power analysis for longitudinal LMMs is often challenging because results depend heavily on parameters such as random slopes and on the…
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Kristoffer Magnusson's blog
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The Cohen's d app translation project
My Cohen's d page supports multiple languages, any help with translating the contents into more languages is very welcome. The contents…
Why linear mixed-effects models are probably not the solution to your missing data problems
Linear mixed-effects models are often used for their ability to handle missing data using maximum likelihood estimation. In this post I will…
Heterogeneous treatment effects and homogeneous outcome variances
Recently there has been a couple of meta-analyses investigating heterogeneous treatment effects by analyzing the ratio of the outcome…
Mediation, confounding, and measurement error
Mediation might be the ultimate example of how a method continues to be used despite a vast number of papers and textbooks describing the…
Change over time is not "treatment response"
This will be a non-technical post illustrating the problems with identifying treatment responders or non-responders using inappropriate…
New paper: The consequences of ignoring therapist effects in longitudinal data analysis
We just published a paper, The consequences of ignoring therapist effects in trials with longitudinal data: A simulation study , where we…
Estimating treatment effects and ICCs from (G)LMMs on the observed scale using Bayes, Part 1: lognormal models
When a multilevel model includes either a non-linear transformation (such as the log-transformation) of the response variable, or of the…
Do you really need a multilevel model? A preview of powerlmm 0.4.0
In this post I will show some of the new simulation features that will be available in powerlmm 0.4.0 . You can already install the dev…
Power analysis for longitudinal multilevel models: powerlmm 0.3.0 is now out on CRAN
My R package powerlmm 0.3.0 is now out on CRAN. It can be installed from CRAN https://cran.r-project.org/package=powerlmm or GitHub…