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Using systematic data categorisation to quantify the types of data collected in clinical trials: the DataCat project
BACKGROUND: Data collection consumes a large proportion of clinical trial resources. Each data item requires time and effort for collection, processing and quality control procedures. In general, more data equals a heavier burden for trial staff and participants. It is also likely to increase costs....
Autores principales: | , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7298750/ https://www.ncbi.nlm.nih.gov/pubmed/32546192 http://dx.doi.org/10.1186/s13063-020-04388-x |
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author | Crowley, Evelyn Treweek, Shaun Banister, Katie Breeman, Suzanne Constable, Lynda Cotton, Seonaidh Duncan, Anne El Feky, Adel Gardner, Heidi Goodman, Kirsteen Lanz, Doris McDonald, Alison Ogburn, Emma Starr, Kath Stevens, Natasha Valente, Marie Fernie, Gordon |
author_facet | Crowley, Evelyn Treweek, Shaun Banister, Katie Breeman, Suzanne Constable, Lynda Cotton, Seonaidh Duncan, Anne El Feky, Adel Gardner, Heidi Goodman, Kirsteen Lanz, Doris McDonald, Alison Ogburn, Emma Starr, Kath Stevens, Natasha Valente, Marie Fernie, Gordon |
author_sort | Crowley, Evelyn |
collection | PubMed |
description | BACKGROUND: Data collection consumes a large proportion of clinical trial resources. Each data item requires time and effort for collection, processing and quality control procedures. In general, more data equals a heavier burden for trial staff and participants. It is also likely to increase costs. Knowing the types of data being collected, and in what proportion, will be helpful to ensure that limited trial resources and participant goodwill are used wisely. AIM: The aim of this study is to categorise the types of data collected across a broad range of trials and assess what proportion of collected data each category represents. METHODS: We developed a standard operating procedure to categorise data into primary outcome, secondary outcome and 15 other categories. We categorised all variables collected on trial data collection forms from 18, mainly publicly funded, randomised superiority trials, including trials of an investigational medicinal product and complex interventions. Categorisation was done independently in pairs: one person having in-depth knowledge of the trial, the other independent of the trial. Disagreement was resolved through reference to the trial protocol and discussion, with the project team being consulted if necessary. KEY RESULTS: Primary outcome data accounted for 5.0% (median)/11.2% (mean) of all data items collected. Secondary outcomes accounted for 39.9% (median)/42.5% (mean) of all data items. Non-outcome data such as participant identifiers and demographic data represented 32.4% (median)/36.5% (mean) of all data items collected. CONCLUSION: A small proportion of the data collected in our sample of 18 trials was related to the primary outcome. Secondary outcomes accounted for eight times the volume of data as the primary outcome. A substantial amount of data collection is not related to trial outcomes. Trialists should work to make sure that the data they collect are only those essential to support the health and treatment decisions of those whom the trial is designed to inform. |
format | Online Article Text |
id | pubmed-7298750 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-72987502020-06-17 Using systematic data categorisation to quantify the types of data collected in clinical trials: the DataCat project Crowley, Evelyn Treweek, Shaun Banister, Katie Breeman, Suzanne Constable, Lynda Cotton, Seonaidh Duncan, Anne El Feky, Adel Gardner, Heidi Goodman, Kirsteen Lanz, Doris McDonald, Alison Ogburn, Emma Starr, Kath Stevens, Natasha Valente, Marie Fernie, Gordon Trials Methodology BACKGROUND: Data collection consumes a large proportion of clinical trial resources. Each data item requires time and effort for collection, processing and quality control procedures. In general, more data equals a heavier burden for trial staff and participants. It is also likely to increase costs. Knowing the types of data being collected, and in what proportion, will be helpful to ensure that limited trial resources and participant goodwill are used wisely. AIM: The aim of this study is to categorise the types of data collected across a broad range of trials and assess what proportion of collected data each category represents. METHODS: We developed a standard operating procedure to categorise data into primary outcome, secondary outcome and 15 other categories. We categorised all variables collected on trial data collection forms from 18, mainly publicly funded, randomised superiority trials, including trials of an investigational medicinal product and complex interventions. Categorisation was done independently in pairs: one person having in-depth knowledge of the trial, the other independent of the trial. Disagreement was resolved through reference to the trial protocol and discussion, with the project team being consulted if necessary. KEY RESULTS: Primary outcome data accounted for 5.0% (median)/11.2% (mean) of all data items collected. Secondary outcomes accounted for 39.9% (median)/42.5% (mean) of all data items. Non-outcome data such as participant identifiers and demographic data represented 32.4% (median)/36.5% (mean) of all data items collected. CONCLUSION: A small proportion of the data collected in our sample of 18 trials was related to the primary outcome. Secondary outcomes accounted for eight times the volume of data as the primary outcome. A substantial amount of data collection is not related to trial outcomes. Trialists should work to make sure that the data they collect are only those essential to support the health and treatment decisions of those whom the trial is designed to inform. BioMed Central 2020-06-16 /pmc/articles/PMC7298750/ /pubmed/32546192 http://dx.doi.org/10.1186/s13063-020-04388-x Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Methodology Crowley, Evelyn Treweek, Shaun Banister, Katie Breeman, Suzanne Constable, Lynda Cotton, Seonaidh Duncan, Anne El Feky, Adel Gardner, Heidi Goodman, Kirsteen Lanz, Doris McDonald, Alison Ogburn, Emma Starr, Kath Stevens, Natasha Valente, Marie Fernie, Gordon Using systematic data categorisation to quantify the types of data collected in clinical trials: the DataCat project |
title | Using systematic data categorisation to quantify the types of data collected in clinical trials: the DataCat project |
title_full | Using systematic data categorisation to quantify the types of data collected in clinical trials: the DataCat project |
title_fullStr | Using systematic data categorisation to quantify the types of data collected in clinical trials: the DataCat project |
title_full_unstemmed | Using systematic data categorisation to quantify the types of data collected in clinical trials: the DataCat project |
title_short | Using systematic data categorisation to quantify the types of data collected in clinical trials: the DataCat project |
title_sort | using systematic data categorisation to quantify the types of data collected in clinical trials: the datacat project |
topic | Methodology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7298750/ https://www.ncbi.nlm.nih.gov/pubmed/32546192 http://dx.doi.org/10.1186/s13063-020-04388-x |
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