Interpreting CA biplots

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 below step by step how to interpret biplots correctly:

Welcome to ClientVPS Mirrors

Interpreting CA biplots

Installation

Stacked Correspondence Analysis

Generating minimal dataset

Investigating different CA configurations using the Shiny application

Plotting CA biplot as ggplot object

Interpreting biplots for Correspondence Analysis

1. Minimum example dataset

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

3. Interpreting dimensions and associations between treatments and AEs

4. Comparing treatments and avoiding misleading interpretations

References