Introducing ‘powerlmm’ an R package for power calculations for longitudinal multilevel models
This article is originally published at http://rpsychologist.com/
Over the years I've produced quite a lot of code for power calculations and simulations of different longitudinal linear mixed models. Over the summer I bundled together these calculations for the designs I most typically encounter into an R package. The purpose of powerlmm is to help design longitudinal treatment studies, with or without higher-level clustering (e.g. by therapists, groups, or physician), and missing data. Currently, powerlmm supports two-level models, nested three-level models, and partially nested models. Additionally, unbalanced designs and missing data can be accounted for in the calculations. Power is calculated analytically, but simulation methods are also provided in order to evaluated bias, type 1 error, and the consequences of model misspecification. For novice R users, the basic functionality is also provided as a Shiny web application.
The package can be install from CRAN: http://cran.r-project.org/package=powerlmm, or GitHub github.com/rpsychologist/powerlmm. Currently, the packages includes three vignettes that show how to setup your studies and calculate power.
# CRAN install.packages("powerlmm") # GitHub devtools::install_github("rpsychologist/powerlmm", build_vignettes = TRUE)
A basic example
library(powerlmm) # dropout per treatment group d <- per_treatment(control = dropout_weibull(0.3, 2), treatment = dropout_weibull(0.2, 2)) # Setup design p <- study_parameters(n1 = 11, # time points n2 = 10, # subjects per cluster n3 = 5, # clusters per treatment arm icc_pre_subject = 0.5, icc_pre_cluster = 0, icc_slope = 0.05, var_ratio = 0.02, dropout = d, cohend = -0.8) # Power get_power(p)
## ## Power calculation for longitudinal linear mixed model (three-level) ## with missing data and unbalanced designs ## ## n1 = 11 ## n2 = 10 (treatment) ## 10 (control) ## n3 = 5 (treatment) ## 5 (control) ## 10 (total) ## total_n = 50 (treatment) ## 50 (control) ## 100 (total) ## dropout = 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 (time) ## 0, 0, 1, 3, 6, 9, 12, 16, 20, 25, 30 (%, control) ## 0, 0, 1, 2, 4, 5, 8, 10, 13, 17, 20 (%, treatment) ## icc_pre_subjects = 0.5 ## icc_pre_clusters = 0 ## icc_slope = 0.05 ## var_ratio = 0.02 ## cohend = -0.8 ## power = 0.68
Feedback
I appreciate all types of feedback, e.g. typos, bugs, inconsistencies, feature requests, etc. Open an issue on github.com/rpsychologist/powerlmm/issues or via my contact info here.
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This article is originally published at http://rpsychologist.com/
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