The current practice of handling and reporting missing outcome data in eight widely used PROMs in RCT publications: a review ... View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2016-01-28

AUTHORS

Ines Rombach, Oliver Rivero-Arias, Alastair M. Gray, Crispin Jenkinson, Órlaith Burke

ABSTRACT

PURPOSE: Patient-reported outcome measures (PROMs) are designed to assess patients' perceived health states or health-related quality of life. However, PROMs are susceptible to missing data, which can affect the validity of conclusions from randomised controlled trials (RCTs). This review aims to assess current practice in the handling, analysis and reporting of missing PROMs outcome data in RCTs compared to contemporary methodology and guidance. METHODS: This structured review of the literature includes RCTs with a minimum of 50 participants per arm. Studies using the EQ-5D-3L, EORTC QLQ-C30, SF-12 and SF-36 were included if published in 2013; those using the less commonly implemented HUI, OHS, OKS and PDQ were included if published between 2009 and 2013. RESULTS: The review included 237 records (4-76 per relevant PROM). Complete case analysis and single imputation were commonly used in 33 and 15 % of publications, respectively. Multiple imputation was reported for 9 % of the PROMs reviewed. The majority of publications (93 %) failed to describe the assumed missing data mechanism, while low numbers of papers reported methods to minimise missing data (23 %), performed sensitivity analyses (22 %) or discussed the potential influence of missing data on results (16 %). CONCLUSIONS: Considerable discrepancy exists between approved methodology and current practice in handling, analysis and reporting of missing PROMs outcome data in RCTs. Greater awareness is needed for the potential biases introduced by inappropriate handling of missing data, as well as the importance of sensitivity analysis and clear reporting to enable appropriate assessments of treatment effects and conclusions from RCTs. More... »

PAGES

1613-1623

References to SciGraph publications

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  • 2013-07-16. A tutorial on sensitivity analyses in clinical trials: the what, why, when and how in BMC MEDICAL RESEARCH METHODOLOGY
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  • 2014-11-19. Handling missing data in RCTs; a review of the top medical journals in BMC MEDICAL RESEARCH METHODOLOGY
  • 2014-06-19. A review of the handling of missing longitudinal outcome data in clinical trials in TRIALS
  • 2010-10-01. Missing data methods for dealing with missing items in quality of life questionnaires. A comparison by simulation of personal mean score, full information maximum likelihood, multiple imputation, and hot deck techniques applied to the SF-36 in the French 2003 decennial health survey in QUALITY OF LIFE RESEARCH
  • 1995-05. The Short-Form 36 (SF-36) Health Survey and Its Use in Pharmacoeconomic Evaluation in PHARMACOECONOMICS
  • 2008-08-11. A review of RCTs in four medical journals to assess the use of imputation to overcome missing data in quality of life outcomes in TRIALS
  • 2012-02-08. The Prevention and Treatment of Missing Data in Clinical Trials: An FDA Perspective on the Importance of Dealing With It in CLINICAL PHARMACOLOGY & THERAPEUTICS
  • 2003-10-16. The Health Utilities Index (HUI®): concepts, measurement properties and applications in HEALTH AND QUALITY OF LIFE OUTCOMES
  • 2008-08-04. Simple imputation methods were inadequate for missing not at random (MNAR) quality of life data in HEALTH AND QUALITY OF LIFE OUTCOMES
  • 1998-04. PDQ-39: a review of the development, validation and application of a Parkinson’s disease quality of life questionnaire and its associated measures in JOURNAL OF NEUROLOGY
  • Identifiers

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    http://scigraph.springernature.com/pub.10.1007/s11136-015-1206-1

    DOI

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    DIMENSIONS

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    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/26821918


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