Current practices in patient-reported outcome (PRO) data collection in clinical trials: a cross-sectional survey of UK trial staff and management
Open accessAbstract
Objectives: Patient-reported outcome measures (PROMs) collected in clinical trials should be administered in a standardised way across sites and routinely screened for avoidable missing data in order to maximise data quality/minimise risk of bias. Recent qualitative findings, however, have raised concerns about the consistency of PROM administration in UK trials. The purpose of this study was to determine the generalisability of these findings across the wider community of trial personnel. Design: Online cross-sectional survey. Setting: Participants were recruited from 55 UK Clinical Research Collaboration Registered Clinical Trials Units and 19 Comprehensive Local Research Networks. Participants: Research nurses, data managers/coordinators, trial managers and chief/principal investigators involved in clinical trials collecting PROMs. Analysis: We undertook descriptive analyses of the quantitative data and directed thematic analysis of free-text comments. Factors associated with the management of missing PRO data were explored using logistic regression. Results: Survey data from 767 respondents supported the generalisability of qualitative study findings, suggesting inconsistencies in PROM administration with regard to: the level of assistance given to trial participants; the timing of PROM completion in relation to the clinical consultation; and the management of missing data. Having ≥10 years experience in a research role was significantly associated with the appropriate management of missing PROM data (OR 2.26 (95% CI 1.06 to 4.82), p=0.035). There was a consensus that more PROM guidance was needed in future trials and agreement between professional groups about the necessary components. Conclusions: There are inconsistencies in the way PROMs are administered by trial staff. Such inconsistencies may reduce the quality of data and have the potential to introduce bias. There is a need for improved guidance in future trials that support trial personnel in conducting optimal PROM data collection to inform patient care.
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There are many problems inherent in the use of patient-reported outcome measures (PROM) in clinical trials.
“There are many problems inherent in the use of patient-reported outcome measures (PROM) in clinical trials [60]”
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- Authors
- Kyte, D., Ives, J., Draper, H., & Calvert, M.
- Affiliations
- University of Birmingham (GB)
- University of Bristol (GB)
- Corresponding author
- Kyte, Derek
- Venue
- BMJ Open
- Publisher
- BMJ
- Published
- 2016-10
- Volume
- 6
- Issue
- 10
- Pages
- e012281
- ISSN
- 2044-6055 (ISSN-L)
- PMC
- PMC5073494
- OpenAlex
- W2529787110
- Cited by
- 46
- References
- 28
- Citations by year
- 2026: 4 · 2025: 2 · 2024: 6 · 2023: 5 · 2022: 7 · 2021: 4 · 2020: 3 · 2019: 6 · 2018: 7 · 2017: 2
- FWCI
- 14.26
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- BMJ Open (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Bristol Research (University of Bristol) (publishedVersion, cc-by)
- HighWire Press Open Archive (submittedVersion)
- University of Birmingham Research Portal (University of Birmingham) (submittedVersion, cc-by)
- Explore Bristol Research (submittedVersion, cc-by)
- Funders
- National Institute for Health Research (School for Primary Care Research Studentship)
- Topics
- Social Sciences › Sociology and Political Science › Survey Methodology and Nonresponse
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Keywords
- patient-reported outcome measures, clinical trials, research nurses, data quality, risk of bias, professional guidance
- MeSH terms
- Data Accuracy, Patient Reported Outcome Measures, Adult, Cross-Sectional Studies, United Kingdom, Humans, Middle Aged, Surveys and Questionnaires, Research Design, Research Personnel, Bias, Biomedical Research, Qualitative Research, Young Adult
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