Abstract
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.
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.
| Original language | English |
|---|---|
| Article number | 535 |
| Pages (from-to) | 535 |
| Number of pages | 10 |
| Journal | Trials |
| Volume | 21 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 16 Jun 2020 |
Bibliographical note
We would like to thank Joanne Palmer and all attendees of the 2015 workshop at the UK Trial Managers’ Network meeting. We thank all Chief Investigators of the trials in our sample for giving their permission to use their trial data collections forms in our analysis: Annie S Anderson (ActWELL), Doreen McClurg (AMBER), Charles Knowles (CONFIDeNT), Augusto AzuaraBlanco (EAGLE), Frank Sullivan (ECLS), Shakila Thangaratinam (EMPiRE), Kevin Cooper (HEALTH), Eugene Dempsey (HIP), Craig Ramsay (iQUAD), Ian Reid (KANECT), David Murray (KAT), Saruban Pasu (PIMS), Khalid S Khan (SALVO), Robert Pickard (SUSPEND), Anthony King (TAGS), Graham Devereux (TWICS), Adrian R Martineau (ViDiFlu), Charis Glazener (VUE). Similarly, we thank the funders of all the trials: Chief Scientist Office (CSO), Scottish Government Health Directorate; CSO, Scottish Government and Oncimmune Ltd; European Commission within the Seventh Framework Programme; National Institute for Health Research - Efficacy and Mechanism Evaluation (NIHR-EME); National Institute for Health Research - Health Technology Assessment (NIHR-HTA) programme; National Institute for Health Research - Programme Grants for Applied Research (NIHR-PGAR); the Scottish Government. The Health Services Research Unit, University of Aberdeen, receives core funding from the CSO of the Scottish Government Health Directorates.Keywords
- PROTOCOL
- IMPACT