Interpreting CA biplots"

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

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:

1. Minimum example dataset

2. Interpreting percentage of explained variability by dimensions, center average treatment and AE dot sizes

knitr::include_graphics("figures/fig2.png")

3. Interpreting dimensions and associations between treatments and AEs

knitr::include_graphics("figures/fig3.png")

4. Comparing treatments and avoiding misleading interpretations

knitr::include_graphics("figures/fig4.png")

References



Try the visae package in your browser

Any scripts or data that you put into this service are public.

visae documentation built on Nov. 11, 2021, 1:10 a.m.