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Visualising harms in publications of randomised controlled trials: consensus and recommendations

OBJECTIVE: To improve communication of harm in publications of randomised controlled trials via the development of recommendations for visually presenting harm outcomes. DESIGN: Consensus study. SETTING: 15 clinical trials units registered with the UK Clinical Research Collaboration, an academic pop...

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Autores principales: Phillips, Rachel, Cro, Suzie, Wheeler, Graham, Bond, Simon, Morris, Tim P, Creanor, Siobhan, Hewitt, Catherine, Love, Sharon, Lopes, Andre, Schlackow, Iryna, Gamble, Carrol, MacLennan, Graeme, Habron, Chris, Gordon, Anthony C, Vergis, Nikhil, Li, Tianjing, Qureshi, Riaz, Everett, Colin C, Holmes, Jane, Kirkham, Amanda, Peckitt, Clare, Pirrie, Sarah, Ahmed, Norin, Collett, Laura, Cornelius, Victoria
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group Ltd. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9108928/
https://www.ncbi.nlm.nih.gov/pubmed/35577357
http://dx.doi.org/10.1136/bmj-2021-068983
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author Phillips, Rachel
Cro, Suzie
Wheeler, Graham
Bond, Simon
Morris, Tim P
Creanor, Siobhan
Hewitt, Catherine
Love, Sharon
Lopes, Andre
Schlackow, Iryna
Gamble, Carrol
MacLennan, Graeme
Habron, Chris
Gordon, Anthony C
Vergis, Nikhil
Li, Tianjing
Qureshi, Riaz
Everett, Colin C
Holmes, Jane
Kirkham, Amanda
Peckitt, Clare
Pirrie, Sarah
Ahmed, Norin
Collett, Laura
Cornelius, Victoria
author_facet Phillips, Rachel
Cro, Suzie
Wheeler, Graham
Bond, Simon
Morris, Tim P
Creanor, Siobhan
Hewitt, Catherine
Love, Sharon
Lopes, Andre
Schlackow, Iryna
Gamble, Carrol
MacLennan, Graeme
Habron, Chris
Gordon, Anthony C
Vergis, Nikhil
Li, Tianjing
Qureshi, Riaz
Everett, Colin C
Holmes, Jane
Kirkham, Amanda
Peckitt, Clare
Pirrie, Sarah
Ahmed, Norin
Collett, Laura
Cornelius, Victoria
author_sort Phillips, Rachel
collection PubMed
description OBJECTIVE: To improve communication of harm in publications of randomised controlled trials via the development of recommendations for visually presenting harm outcomes. DESIGN: Consensus study. SETTING: 15 clinical trials units registered with the UK Clinical Research Collaboration, an academic population health department, Roche Products, and The BMJ. PARTICIPANTS: Experts in clinical trials: 20 academic statisticians, one industry statistician, one academic health economist, one data graphics designer, and two clinicians. MAIN OUTCOME: measures A methodological review of statistical methods identified visualisations along with those recommended by consensus group members. Consensus on visual recommendations was achieved (at least 60% of the available votes) over a series of three meetings with participants. The participants reviewed and critically appraised candidate visualisations against an agreed framework and voted on whether to endorse each visualisation. Scores marginally below this threshold (50-60%) were revisited for further discussions and votes retaken until consensus was reached. RESULTS: 28 visualisations were considered, of which 10 are recommended for researchers to consider in publications of main research findings. The choice of visualisations to present will depend on outcome type (eg, binary, count, time-to-event, or continuous), and the scenario (eg, summarising multiple emerging events or one event of interest). A decision tree is presented to assist trialists in deciding which visualisations to use. Examples are provided of each endorsed visualisation, along with an example interpretation, potential limitations, and signposting to code for implementation across a range of standard statistical software. Clinician feedback was incorporated into the explanatory information provided in the recommendations to aid understanding and interpretation. CONCLUSIONS: Visualisations provide a powerful tool to communicate harms in clinical trials, offering an alternative perspective to the traditional frequency tables. Increasing the use of visualisations for harm outcomes in clinical trial manuscripts and reports will provide clearer presentation of information and enable more informative interpretations. The limitations of each visualisation are discussed and examples of where their use would be inappropriate are given. Although the decision tree aids the choice of visualisation, the statistician and clinical trial team must ultimately decide the most appropriate visualisations for their data and objectives. Trialists should continue to examine crude numbers alongside visualisations to fully understand harm profiles.
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spelling pubmed-91089282022-05-27 Visualising harms in publications of randomised controlled trials: consensus and recommendations Phillips, Rachel Cro, Suzie Wheeler, Graham Bond, Simon Morris, Tim P Creanor, Siobhan Hewitt, Catherine Love, Sharon Lopes, Andre Schlackow, Iryna Gamble, Carrol MacLennan, Graeme Habron, Chris Gordon, Anthony C Vergis, Nikhil Li, Tianjing Qureshi, Riaz Everett, Colin C Holmes, Jane Kirkham, Amanda Peckitt, Clare Pirrie, Sarah Ahmed, Norin Collett, Laura Cornelius, Victoria BMJ Research OBJECTIVE: To improve communication of harm in publications of randomised controlled trials via the development of recommendations for visually presenting harm outcomes. DESIGN: Consensus study. SETTING: 15 clinical trials units registered with the UK Clinical Research Collaboration, an academic population health department, Roche Products, and The BMJ. PARTICIPANTS: Experts in clinical trials: 20 academic statisticians, one industry statistician, one academic health economist, one data graphics designer, and two clinicians. MAIN OUTCOME: measures A methodological review of statistical methods identified visualisations along with those recommended by consensus group members. Consensus on visual recommendations was achieved (at least 60% of the available votes) over a series of three meetings with participants. The participants reviewed and critically appraised candidate visualisations against an agreed framework and voted on whether to endorse each visualisation. Scores marginally below this threshold (50-60%) were revisited for further discussions and votes retaken until consensus was reached. RESULTS: 28 visualisations were considered, of which 10 are recommended for researchers to consider in publications of main research findings. The choice of visualisations to present will depend on outcome type (eg, binary, count, time-to-event, or continuous), and the scenario (eg, summarising multiple emerging events or one event of interest). A decision tree is presented to assist trialists in deciding which visualisations to use. Examples are provided of each endorsed visualisation, along with an example interpretation, potential limitations, and signposting to code for implementation across a range of standard statistical software. Clinician feedback was incorporated into the explanatory information provided in the recommendations to aid understanding and interpretation. CONCLUSIONS: Visualisations provide a powerful tool to communicate harms in clinical trials, offering an alternative perspective to the traditional frequency tables. Increasing the use of visualisations for harm outcomes in clinical trial manuscripts and reports will provide clearer presentation of information and enable more informative interpretations. The limitations of each visualisation are discussed and examples of where their use would be inappropriate are given. Although the decision tree aids the choice of visualisation, the statistician and clinical trial team must ultimately decide the most appropriate visualisations for their data and objectives. Trialists should continue to examine crude numbers alongside visualisations to fully understand harm profiles. BMJ Publishing Group Ltd. 2022-05-16 /pmc/articles/PMC9108928/ /pubmed/35577357 http://dx.doi.org/10.1136/bmj-2021-068983 Text en © Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY. No commercial re-use. See rights and permissions. Published by BMJ. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed in accordance with the terms of the Creative Commons Attribution (CC BY 4.0) license, which permits others to distribute, remix, adapt and build upon this work, for commercial use, provided the original work is properly cited. See: http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Research
Phillips, Rachel
Cro, Suzie
Wheeler, Graham
Bond, Simon
Morris, Tim P
Creanor, Siobhan
Hewitt, Catherine
Love, Sharon
Lopes, Andre
Schlackow, Iryna
Gamble, Carrol
MacLennan, Graeme
Habron, Chris
Gordon, Anthony C
Vergis, Nikhil
Li, Tianjing
Qureshi, Riaz
Everett, Colin C
Holmes, Jane
Kirkham, Amanda
Peckitt, Clare
Pirrie, Sarah
Ahmed, Norin
Collett, Laura
Cornelius, Victoria
Visualising harms in publications of randomised controlled trials: consensus and recommendations
title Visualising harms in publications of randomised controlled trials: consensus and recommendations
title_full Visualising harms in publications of randomised controlled trials: consensus and recommendations
title_fullStr Visualising harms in publications of randomised controlled trials: consensus and recommendations
title_full_unstemmed Visualising harms in publications of randomised controlled trials: consensus and recommendations
title_short Visualising harms in publications of randomised controlled trials: consensus and recommendations
title_sort visualising harms in publications of randomised controlled trials: consensus and recommendations
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9108928/
https://www.ncbi.nlm.nih.gov/pubmed/35577357
http://dx.doi.org/10.1136/bmj-2021-068983
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