knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.path = "man/figures/README-", out.width = "100%" )
The goal of consort
is to make it easy to create CONSORT diagrams for the transparent reporting of participant allocation in randomized, controlled clinical trials. This is done by creating a standardized disposition data, and using this data as the source for the creation a standard CONSORT diagram. Human effort by supplying text labels on the node can also be achieved.
You can install the released version of consort from CRAN with:
install.packages("consort")
And the development version from GitHub with:
# install.packages("devtools") devtools::install_github("adayim/consort")
This is a basic example which shows you how to solve a create CONSORT diagram with a given subject disposition data:
library(consort) ## basic example code
set.seed(1001) N <- 300 trialno <- sample(c(1000:2000), N) exc <- rep(NA, N) exc[sample(1:N, 15)] <- sample(c("Sample not collected", "MRI not collected", "Other"), 15, replace = T, prob = c(0.4, 0.4, 0.2)) arm <- rep(NA, N) arm[is.na(exc)] <- sample(c("Conc", "Seq"), sum(is.na(exc)), replace = T) fow1 <- rep(NA, N) fow1[!is.na(arm)] <- sample(c("Withdraw", "Discontinued", "Death", "Other", NA), sum(!is.na(arm)), replace = T, prob = c(0.05, 0.05, 0.05, 0.05, 0.8)) fow2 <- rep(NA, N) fow2[!is.na(arm) & is.na(fow1)] <- sample(c("Protocol deviation", "Outcome missing", NA), sum(!is.na(arm) & is.na(fow1)), replace = T, prob = c(0.05, 0.05, 0.9)) df <- data.frame(trialno, exc, arm, fow1, fow2) head(df)
out <- consort_plot(data = df, order = c(trialno = "Population", exc = "Excluded", arm = "Randomized patient", fow1 = "Lost of Follow-up", trialno = "Finished Followup", fow2 = "Not evaluable", trialno = "Final Analysis"), side_box = c("exc", "fow1", "fow2"), allocation = "arm", labels = c("1" = "Screening", "2" = "Randomization", "5" = "Final"), cex = 0.6) plot(out)
As the grid
plotting is not very ideal, calculation of the coodinates for the nodes are not easy job and tried my best. Feel free to PR if you want to improve. Or you can produce Graphviz
plot by setting grViz = TRUE
in plot
. This will use DiagrammeR
to print the plot. The plot is ideal for Shiny or HTML output.
plot(out, grViz = TRUE)
Or save this Graphviz
plot to png
or pdf
plot(g, grViz = TRUE) |> DiagrammeRsvg::export_svg() |> charToRaw() |> rsvg::rsvg_pdf("svg_graph.pdf")
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