The R package visae implements 'shiny' apps to visualize adverse events (AE) based on the Common Terminology Criteria for Adverse Events (CTCAE).
instal.packages("visae")The latest version can be installed from GitHub as follows:
devtools::install_github("dnzmarcio/visae")patient_id <- 1:4000
group <- c(rep("A", 1000), rep("B", 1000), rep("C", 1000), rep("D", 1000))
ae_grade <- c(rep("AE class 01", 600), rep("AE class 02", 300),
rep("AE class 03", 100), rep("AE class 04", 0),
rep("AE class 01", 100), rep("AE class 02", 400),
rep("AE class 03", 400), rep("AE class 04", 100),
rep("AE class 01", 233), rep("AE class 02", 267),
rep("AE class 03", 267), rep("AE class 04", 233),
rep("AE class 01", 0), rep("AE class 02", 100),
rep("AE class 03", 300), rep("AE class 04", 600))
dt <- tibble(patient_id = patient_id, trt = group, ae_g = ae_grade)library(visae)
library(magrittr)
library(dplyr)
dt %>% run_ca(., group = trt,
id = patient_id,
ae_grade = ae_g)ca <- dt %>% ca_ae(., group = trt,
id = patient_id,
ae_class = ae_g,
contr_indicator = FALSE,
mass_indicator = TRUE,
contr_threshold = 0,
mass_threshold = 0)
ca$asymmetric_plotInvestigators often interpret CA biplots erroneously assuming that the distance between AE classes dots and treatments dots is an indicative of association. See step by step to interpret biplots correctly are below:
2. Interpreting percentage of explained variability by dimensions, center average treatment and AE dot sizes
