Design, implementation and reporting strategies to reduce the instance and impact of missing patient-reported outcome (PRO) data: A systematic review

Melanie Calvert, Rebecca Mercieca-bebber, Michael Brundage, Madeleine King

    Research output: Contribution to journalArticlepeer-review

    37 Citations (Scopus)
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    Abstract

    OBJECTIVES
    Patient-reported outcomes (PROs) provide important information about the impact of treatment from the patient’s perspective. However missing PRO data may compromise the interpretability and value of findings. We aimed to report: 1) a non-technical summary of problems caused by missing PRO data; and 2)a systematic review collating strategies to: a) minimise rates of missing PRO data; and b) facilitate transparent interpretation and reporting of missing PRO data in clinical research. Our systematic review does not address statistical handling of missing PRO data.
    DATA SOURCES
    Medline and Cumulative Index to Nursing and Allied Health Literature (CINAHL) databases (inception to 31 March 2015), and citing articles and reference lists of relevant sources.
    ELIGIBILITY CRITERIA
    English articles providing recommendations for reducing missing PRO data rates, or strategies to facilitate transparent interpretation and reporting of missing PRO data were included.
    METHODS
    Two reviewers independently screened articles against eligibility criteria. Discrepancies were resolved with the research team. Recommendations were extracted and coded according to Framework Synthesis.
    RESULTS
    117 sources (55% discussion papers, 26% original research) met the eligibility criteria. Design and methodological strategies for reducing rates of missing PRO data included: incorporating PRO-specific information in the protocol; carefully designing PRO assessment schedules and defining termination rules; minimising patient burden; appointing a PRO coordinator; PRO-specific training for staff; ensuring PRO studies are adequately resourced ; and continuous quality assurance. Strategies for transparent interpretation and reporting of missing PRO data include: utilising auxiliary data to inform analysis; transparently reporting baseline PRO scores, rates and reasons for missing data and methods for handling missing PRO data.
    CONCLUSIONS
    The instance of missing PRO data and its potential to bias clinical research can be minimised by implementing thoughtful design, rigorous methodology and transparent reporting strategies. All members of the research team have a responsibility in implementing such strategies.

    ARTICLE SUMMARY: STRENGTHS AND LIMITATIONS
    1.This is the first systematic review to collate practical strategies to minimise the problem of missing patient-reported outcome (PRO) data, with potential drawbacks of each recommendation, encompassing 117 multidisciplinary sources.
    2.Missing PRO data may be preventable in many cases by implementing rigorous study design and methodological strategies described in this review.
    3.In some clinical research settings, missing PRO data is not avoidable, due to deteriorating health status of the participants. Strategies to minimise the potential for bias caused by missing PRO data are described.
    4.This paper discusses one aspect of PRO data quality: data completeness. Many other factors also contribute to high-quality PRO data, including but not limited to appropriateness of PRO measures, timing of PRO assessment, ensuring patients self-complete and clinical versus statistical significance of findings.
    5.This review excludes non-English sources. Additional, non-English publications may have been relevant, however given the repetition of themes found in our 117 included sources we do not believe that these would significantly affect our findings.
    Original languageEnglish
    Article numbere010938
    JournalBritish Medical Journal Open
    Volume6
    DOIs
    Publication statusPublished - 15 Jun 2016

    Keywords

    • Author has been contacted and asked for the availability of a post-print
    • Quality of Life
    • Missing data
    • quality assurance
    • methodology

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