Cited by 46Survey Methodology and NonresponseStatistical Methods in Clinical TrialsMeta-analysis and systematic reviews

Current practices in patient-reported outcome (PRO) data collection in clinical trials: a cross-sectional survey of UK trial staff and management

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Kyte, Derek, Ives, Jonathan, Draper, Heather, Calvert, Melanie·2016·BMJ Open

Abstract

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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  1. [7]Bernhard J, Cella DF, Coates AS Missing quality of life data in cancer clinical trials: serious problems and challenges. Stat Med 1998; 17: 517– 32. 10.1002/(SICI)1097-0258(19980315/15)17:5/7<517::AID-SIM799>3.0.CO;2-S 9549801 · DOI
  2. [8]Bernhard J, Peterson HF, Coates AS Quality of life assessment in International Breast Cancer Study Group (IBCSG) trials: practical issues and factors associated with missing data. Stat Med 1998; 17: 587– 601. 10.1002/(SICI)1097-0258(19980315/15)17:5/7<587::AID-SIM806>3.0.CO;2-# 9549808 · DOI
  3. [10]Simes RJ, Greatorex V, Gebski VJ Practical approaches to minimize problems with missing quality of life data. Stat Med 1998; 17: 725– 37. 10.1002/(SICI)1097-0258(19980315/15)17:5/7<725::AID-SIM817>3.0.CO;2-1 9549819 · DOI

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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)
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
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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