Visualizing adverse events in clinical trials using correspondence analysis with R-package visae

Abstract Background Graphical displays and data visualization are essential components of statistical analysis that can lead to improved understanding of clinical trial adverse event (AE) data. Correspondence analysis (CA) has been introduced decades ago as a multivariate technique that can communic...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Márcio A. Diniz, Gillian Gresham, Sungjin Kim, Michael Luu, N. Lynn Henry, Mourad Tighiouart, Greg Yothers, Patricia A. Ganz, André Rogatko
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	Data visualization Correspondence analysis Adverse event CTCAE Clinical trials Medicine (General) R5-920
Acceso en línea:	https://doaj.org/article/645aeae8d36249c3b67e29f5fc113c33
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:645aeae8d36249c3b67e29f5fc113c33
record_format	dspace
spelling	oai:doaj.org-article:645aeae8d36249c3b67e29f5fc113c332021-11-14T12:39:35ZVisualizing adverse events in clinical trials using correspondence analysis with R-package visae10.1186/s12874-021-01368-w1471-2288https://doaj.org/article/645aeae8d36249c3b67e29f5fc113c332021-11-01T00:00:00Zhttps://doi.org/10.1186/s12874-021-01368-whttps://doaj.org/toc/1471-2288Abstract Background Graphical displays and data visualization are essential components of statistical analysis that can lead to improved understanding of clinical trial adverse event (AE) data. Correspondence analysis (CA) has been introduced decades ago as a multivariate technique that can communicate AE contingency tables using two-dimensional plots, while quantifying the loss of information as other dimension reduction techniques such as principal components and factor analysis. Methods We propose the application of 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, visae, publicly available on Comprehensive R Archive Network (CRAN), to interactively investigate CA configurations based on the contribution to the explained variance and relative frequency of AEs. Data from two randomized controlled trials (RCT) were used to illustrate the proposed methods: NSABP R-04, a neoadjuvant rectal 2 × 2 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. Results 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. Conclusion 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.Márcio A. DinizGillian GreshamSungjin KimMichael LuuN. Lynn HenryMourad TighiouartGreg YothersPatricia A. GanzAndré RogatkoBMCarticleData visualizationCorrespondence analysisAdverse eventCTCAEClinical trialsMedicine (General)R5-920ENBMC Medical Research Methodology, Vol 21, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Data visualization Correspondence analysis Adverse event CTCAE Clinical trials Medicine (General) R5-920
spellingShingle	Data visualization Correspondence analysis Adverse event CTCAE Clinical trials Medicine (General) R5-920 Márcio A. Diniz Gillian Gresham Sungjin Kim Michael Luu N. Lynn Henry Mourad Tighiouart Greg Yothers Patricia A. Ganz André Rogatko Visualizing adverse events in clinical trials using correspondence analysis with R-package visae
description	Abstract Background Graphical displays and data visualization are essential components of statistical analysis that can lead to improved understanding of clinical trial adverse event (AE) data. Correspondence analysis (CA) has been introduced decades ago as a multivariate technique that can communicate AE contingency tables using two-dimensional plots, while quantifying the loss of information as other dimension reduction techniques such as principal components and factor analysis. Methods We propose the application of 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, visae, publicly available on Comprehensive R Archive Network (CRAN), to interactively investigate CA configurations based on the contribution to the explained variance and relative frequency of AEs. Data from two randomized controlled trials (RCT) were used to illustrate the proposed methods: NSABP R-04, a neoadjuvant rectal 2 × 2 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. Results 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. Conclusion 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.
format	article
author	Márcio A. Diniz Gillian Gresham Sungjin Kim Michael Luu N. Lynn Henry Mourad Tighiouart Greg Yothers Patricia A. Ganz André Rogatko
author_facet	Márcio A. Diniz Gillian Gresham Sungjin Kim Michael Luu N. Lynn Henry Mourad Tighiouart Greg Yothers Patricia A. Ganz André Rogatko
author_sort	Márcio A. Diniz
title	Visualizing adverse events in clinical trials using correspondence analysis with R-package visae
title_short	Visualizing adverse events in clinical trials using correspondence analysis with R-package visae
title_full	Visualizing adverse events in clinical trials using correspondence analysis with R-package visae
title_fullStr	Visualizing adverse events in clinical trials using correspondence analysis with R-package visae
title_full_unstemmed	Visualizing adverse events in clinical trials using correspondence analysis with R-package visae
title_sort	visualizing adverse events in clinical trials using correspondence analysis with r-package visae
publisher	BMC
publishDate	2021
url	https://doaj.org/article/645aeae8d36249c3b67e29f5fc113c33
work_keys_str_mv	AT marcioadiniz visualizingadverseeventsinclinicaltrialsusingcorrespondenceanalysiswithrpackagevisae AT gilliangresham visualizingadverseeventsinclinicaltrialsusingcorrespondenceanalysiswithrpackagevisae AT sungjinkim visualizingadverseeventsinclinicaltrialsusingcorrespondenceanalysiswithrpackagevisae AT michaelluu visualizingadverseeventsinclinicaltrialsusingcorrespondenceanalysiswithrpackagevisae AT nlynnhenry visualizingadverseeventsinclinicaltrialsusingcorrespondenceanalysiswithrpackagevisae AT mouradtighiouart visualizingadverseeventsinclinicaltrialsusingcorrespondenceanalysiswithrpackagevisae AT gregyothers visualizingadverseeventsinclinicaltrialsusingcorrespondenceanalysiswithrpackagevisae AT patriciaaganz visualizingadverseeventsinclinicaltrialsusingcorrespondenceanalysiswithrpackagevisae AT andrerogatko visualizingadverseeventsinclinicaltrialsusingcorrespondenceanalysiswithrpackagevisae
_version_	1718429109957165056

Visualizing adverse events in clinical trials using correspondence analysis with R-package visae

Ejemplares similares