randotools

randotools contains functions for creating randomisation lists, and other related tasks, in R.

Installation

You can install the development version of randotools from github with:

remotes::install_github("CTU-Bern/randotools")

Generating randomization lists

Generate the randomisation list itself with randolist.

library(randotools)
set.seed(123)
r <- randolist(50, arms = c("Trt1", "Trt2"), strata = list(sex = c("Female", "Male")))

Create a short summary of the randomisation list with summary.

summary(r)
#> ---- Randomisation list report ----
#> -- Overall
#> Total number of randomisations:  106 
#> Randomisation groups:  Trt1 : Trt2 
#> Randomisation ratio: 1:1 
#> Randomisations to each arm:
#> Trt1 Trt2 
#>   53   53 
#> Block sizes:
#>  2  4  6 
#>  9 13  6 
#> -- Stratifier level 
#> Randomisation list is stratified by variables sex 
#> -  1 
#> Randomisations per level of sex :
#> Female   Male 
#>     54     52 
#> Balance per level of sex :        
#>          Trt1 Trt2
#>   Female   27   27
#>   Male     26   26
#> -- Stratum level 
#> 2 strata are defined:
#> 
#> Female   Male 
#>     54     52 
#> -  Female 
#> Number of randomisations:  54
#> Trt1 Trt2 
#>   27   27 
#> Block sizes: 
#> 2 4 6 
#> 5 5 4 
#> -  Male 
#> Number of randomisations:  52
#> Trt1 Trt2 
#>   26   26 
#> Block sizes: 
#> 2 4 6 
#> 4 8 2

Export the randomisation list in a database compatible format with randolist_to_db.

randolist_to_db(r, target_db = "REDCap",
                strata_enc = list(sex = data.frame(sex = c("Female", "Male"),
                                                   code = 1:2)),
                rando_enc = data.frame(arm = c("Trt1", "Trt2"),
                                       rand_result = 1:2)
                )

Assessing randomisation imbalance

Under some scenarios (e.g. minimization), it can be helpful to monitor the imbalance of the randomisation process. randotools includes some tools to assist with this, specifically in terms of comparing observed imbalance with what might be observed with simple random allocation.

imbalance_seq_plots shows the evolution of imbalance along the randomisation sequence:

data(rando_balance)
imbalance_seq_plots(rando_balance, "rando_res")

imbalance_test compares the observed imbalance with random allocation, providing a p-value, which can then be plotted to inspect the observed (the red line) and simulated imbalances (the bars):

(imb <- imbalance_test(rando_balance, "rando_res"))
#> Randomisations to date: 100 
#> Overall imbalance: 0 
#>   Probability of equal or less imbalance from random allocation: 0.072
imbalance_test_plot(imb)

Acknowledgements

Development of the package was funded in part via a grant from the Swiss Clinical Trial Organization Statistics and Methodology Platform.

The package logo was created with ggplot2 and hexSticker with icons from Font Awesome.