  We propose to apply stacked CA using contribution biplots as a tool to
explore differences in AE data among treatments in clinical trials. We defined
five levels of refinement for the analysis based on data derived from the
Common Terminology Criteria for Adverse Events (CTCAE) grades, domains, terms
and their combinations. In addition, we developed a Shiny app built in an
R-package, publicly available on Comprehensive R Archive Network (CRAN), to
interactively investigate CA configurations. Data from two randomized
controlled trials (RCT) were used to illustrate the proposed methods: NSABP
R-04, a neoadjuvant rectal 2x2 factorial trial comparing radiation therapy with
either capecitabine (Cape) or 5-fluorouracil (5-FU) alone with or without
oxaliplatin (Oxa), and NSABP B-35, a double-blind RCT comparing tamoxifen to
anastrozole in postmenopausal women with hormone-positive ductal carcinoma in
situ. In the R04 trial (n=1308), CA biplots displayed the discrepancies between
single agent treatments and their combinations with Oxa at all levels of AE
classes, such that these discrepancies were responsible for the largest portion
of the explained variability among treatments. In addition, an interaction
effect when adding Oxa to Cape/5-FU was identified when the distance between
Cape+Oxa and 5-FU+Oxa was observed to be larger than the distance between 5-FU
and Cape, with Cape+Oxa and 5-FU+Oxa in different quadrants of the CA biplots.
In the B35 trial (n=3009), CA biplots showed different patterns for
non-adherent Anastrozole and Tamoxifen compared with their adherent
counterparts. CA with contribution biplot is an effective tool that can be used
to summarize AE data in a two-dimensional display while minimizing the loss of
information and interpretation.
