Multilevel
9 posts
Introducing PowerLMM.js: Browser-based power analysis for longitudinal models with dropout
A new tool for power analysis of longitudinal linear mixed-effects models (LMMs), with support for missing data, random slopes, and non-inferiority and equivalence tests.
Change over time is not "treatment response"
The term "treatment response" is both easy to understand and simultaneously often used when causal language is clearly unwarranted. In this post, I present a non-technical example of when a naïve subgroup analysis leads to the wrong conclusion that a subgroup of patients is treatment non-responders.
Estimating treatment effects and ICCs from (G)LMMs on the observed scale using Bayes, Part 1: lognormal models
In this post I show how to make marginal inferences on the untransformed scaled when using multilevel models with a non-linear transformation applied to the dependent variable (a log-transformation is used as an example). Cluster-specific versus population-average (conditional versus marginal) effects are compared using both average effects on the untransformed scale and using relative (multiplicative) effects.
Do you really need a multilevel model? A preview of powerlmm 0.4.0
The next version of powerlmm (0.4.0) will soon be released, besides bug fixes this version also includes several new simulation features. In this post I will show two examples that cover the major new features.
Power analysis for longitudinal multilevel models: powerlmm 0.3.0 is now out on CRAN
My R package 'powerlmm' has now been update to version 0.3.0. It adds support for a more flexible effect size specifiation.
Slides from my talk on how to do power analysis for longitudinal 2- and 3-level models.
This post contains the slides from a talk I gave recently at Stockholm University
Power analysis for longitudinal multilevel models: powerlmm 0.2.0 is now out on CRAN
My R package 'powerlmm' has now been update to version 0.2.0. It contains several improvements, and new features.
Introducing 'powerlmm' an R package for power calculations for longitudinal multilevel models
Over the summer I've been working on finishing my new R package 'powerlmm', which is now almost complete. It provides flexible power calculations for typical two- and three-level longitudinal linear mixed models, with unbalanced treatment groups and cluster sizes, as well as with missing data and random slopes at both the subject and cluster-level.
Using R and lme/lmer to fit different two- and three-level longitudinal models
I often get asked about how to fit different longitudinal models in lme/lmer. In this post I cover several different two-level, three-level and partially nested models.