Victoria Cornelius1, Suzie Cro2, Rachel Phillips2. 1. Imperial Clinical Trials Unit, Imperial College London, 1st Floor Stadium House, 68 Wood Lane, London, W12 7RH, UK. v.cornelius@imperial.ac.uk. 2. Imperial Clinical Trials Unit, Imperial College London, 1st Floor Stadium House, 68 Wood Lane, London, W12 7RH, UK.
Abstract
BACKGROUND: Randomised controlled trials (RCTs) provide valuable information and inform the development of harm profiles of new treatments. Harms are typically assessed through the collection of adverse events (AEs). Despite AEs being routine outcomes collected in trials, analysis and reporting of AEs in journal articles are continually shown to be suboptimal. One key challenge is the large volume of AEs, which can make evaluation and communication problematic. Prominent practice is to report frequency tables of AEs by arm. Visual displays offer an effective solution to assess and communicate complex information; however, they are rarely used and there is a lack of practical guidance on what and how to visually display complex AE data. METHODS: In this article, we demonstrate the use of two plots identified to be beneficial for wide use in RCTs, since both can display multiple AEs and are suitable to display point estimates for binary, count, or time-to-event AE data: the volcano and dot plots. We compare and contrast the use of data visualisations against traditional frequency table reporting, using published AE information in two placebo-controlled trials, of remdesivir for COVID-19 and GDNF for Parkinson disease. We introduce statistical programmes for implementation in Stata. RESULTS/CASE STUDY: Visualisations of AEs in the COVID-19 trial communicated a risk profile for remdesivir which differed from the main message in the published authors' conclusion. In the Parkinson's disease trial of GDNF, the visualisation provided immediate communication of harm signals, which had otherwise been contained within lengthy descriptive text and tables. Asymmetry in the volcano plot helped flag extreme events that were less obvious from review of the frequency table and dot plot. The dot plot allowed a more comprehensive representation by means of a more detailed summary. CONCLUSIONS: Visualisations can better support investigators to assimilate large volumes of data and enable improved informal between-arm comparisons compared to tables. We endorse increased uptake for use in trial publications. Care in construction of visual displays needs to be taken as there can be potential to overemphasise treatment effects in some circumstances.
BACKGROUND: Randomised controlled trials (RCTs) provide valuable information and inform the development of harm profiles of new treatments. Harms are typically assessed through the collection of adverse events (AEs). Despite AEs being routine outcomes collected in trials, analysis and reporting of AEs in journal articles are continually shown to be suboptimal. One key challenge is the large volume of AEs, which can make evaluation and communication problematic. Prominent practice is to report frequency tables of AEs by arm. Visual displays offer an effective solution to assess and communicate complex information; however, they are rarely used and there is a lack of practical guidance on what and how to visually display complex AE data. METHODS: In this article, we demonstrate the use of two plots identified to be beneficial for wide use in RCTs, since both can display multiple AEs and are suitable to display point estimates for binary, count, or time-to-event AE data: the volcano and dot plots. We compare and contrast the use of data visualisations against traditional frequency table reporting, using published AE information in two placebo-controlled trials, of remdesivir for COVID-19 and GDNF for Parkinson disease. We introduce statistical programmes for implementation in Stata. RESULTS/CASE STUDY: Visualisations of AEs in the COVID-19 trial communicated a risk profile for remdesivir which differed from the main message in the published authors' conclusion. In the Parkinson's disease trial of GDNF, the visualisation provided immediate communication of harm signals, which had otherwise been contained within lengthy descriptive text and tables. Asymmetry in the volcano plot helped flag extreme events that were less obvious from review of the frequency table and dot plot. The dot plot allowed a more comprehensive representation by means of a more detailed summary. CONCLUSIONS: Visualisations can better support investigators to assimilate large volumes of data and enable improved informal between-arm comparisons compared to tables. We endorse increased uptake for use in trial publications. Care in construction of visual displays needs to be taken as there can be potential to overemphasise treatment effects in some circumstances.
Authors: Neil Lineberry; Jesse A Berlin; Bernadette Mansi; Susan Glasser; Michael Berkwits; Christian Klem; Ananya Bhattacharya; Leslie Citrome; Robert Enck; John Fletcher; Daniel Haller; Tai-Tsang Chen; Christine Laine Journal: BMJ Date: 2016-10-03
Authors: Alan Whone; Matthias Luz; Mihaela Boca; Max Woolley; Lucy Mooney; Sonali Dharia; Jack Broadfoot; David Cronin; Christian Schroers; Neil U Barua; Lara Longpre; C Lynn Barclay; Chris Boiko; Greg A Johnson; H Christian Fibiger; Rob Harrison; Owen Lewis; Gemma Pritchard; Mike Howell; Charlie Irving; David Johnson; Suk Kinch; Christopher Marshall; Andrew D Lawrence; Stephan Blinder; Vesna Sossi; A Jon Stoessl; Paul Skinner; Erich Mohr; Steven S Gill Journal: Brain Date: 2019-03-01 Impact factor: 13.501
Authors: Noel Patson; Mavuto Mukaka; Kennedy N Otwombe; Lawrence Kazembe; Don P Mathanga; Victor Mwapasa; Alinune N Kabaghe; Marinus J C Eijkemans; Miriam K Laufer; Tobias Chirwa Journal: Malar J Date: 2020-03-20 Impact factor: 2.979
Authors: Rachel Phillips; Suzie Cro; Graham Wheeler; Simon Bond; Tim P Morris; Siobhan Creanor; Catherine Hewitt; Sharon Love; Andre Lopes; Iryna Schlackow; Carrol Gamble; Graeme MacLennan; Chris Habron; Anthony C Gordon; Nikhil Vergis; Tianjing Li; Riaz Qureshi; Colin C Everett; Jane Holmes; Amanda Kirkham; Clare Peckitt; Sarah Pirrie; Norin Ahmed; Laura Collett; Victoria Cornelius Journal: BMJ Date: 2022-05-16