Description
Visualization of Adverse Events.
Description
Implementation of 'shiny' app to visualize adverse events based on the Common Terminology Criteria for Adverse Events (CTCAE) using stacked correspondence analysis as described in Diniz et. al (2021)<doi:10.1186/s12874-021-01368-w>.
README.md
Visualizing Adserve Events
The R package visae
implements 'shiny' apps to visualize adverse events (AE) based on the Common Terminology Criteria for Adverse Events (CTCAE).
Installation
instal.packages("visae")
The latest version can be installed from GitHub as follows:
devtools::install_github("dnzmarcio/visae")
Stacked Correspondence Analysis
Generating minimal dataset
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)
Investigating different CA configurations using the Shiny application
library(visae)
library(magrittr)
library(dplyr)
dt %>% run_ca(., group = trt,
id = patient_id,
ae_grade = ae_g)
Plotting CA biplot as ggplot object
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_plot
Interpreting biplots for Correspondence Analysis
Investigators 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: