Longitudinal
7 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.
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.