Abstract
AbstractRisk of bias tools is important in identifying inherent methodical flaws and for generating evidence in studies involving systematic reviews (SRs) and meta-analyses (MAs), hence the need for sensitive and study-specific tools. This study aimed to review quality assessment (QA) tools used in SRs and MAs involving real-world data. Electronic databases involving PubMed, Allied and Complementary Medicine Database, Cumulated Index to Nursing and Allied Health Literature, and MEDLINE were searched for SRs and MAs involving real-world data. Search was delimited to articles published in English, and between inception to 20 of November 2022 following the SRs and MAs extension for scoping checklist. Sixteen articles on real-world data published between 2016 and 2021 that reported their methodological quality met the inclusion criteria. Seven of these articles were observational studies, while the others were of interventional type. Overall, 16 QA tools were identified. Except one, all the QA tools employed in SRs and MAs involving real-world data are generic, and only three of these were validated. Generic QA tools are mostly used for real-world data SRs and MAs, while no validated and reliable specific tool currently exist. Thus, there is need for a standardized and specific QA tool of SRs and MAs for real-world data.
Publisher
Springer Science and Business Media LLC
Subject
Immunology,Immunology and Allergy,Rheumatology
Reference46 articles.
1. Manchikanti L (2008) Evidence-based medicine, systematic reviews, and guidelines in interventional pain management, part I: introduction and general considerations. Pain Physician 11(2):161
2. Oxman AD, Schünemann HJ, Fretheim A (2006) Improving the use of research evidence in guideline development: 8. Synthesis and presentation of evidence. Health Res Policy Syst 4(1):1–10
3. Moosapour H, Saeidifard F, Aalaa M, Soltani A, Larijani B (2021) The rationale behind systematic reviews in clinical medicine: a conceptual framework. J Diabetes Metab Disord 20:919–929
4. Michelson M, Reuter K (2019) The significant cost of systematic reviews and meta-analyses: a call for greater involvement of machine learning to assess the promise of clinical trials. Contemp Clin Trials Commun 16:100443
5. Chinnock P, Siegfried N, Clarke M (2005) Is evidence-based medicine relevant to the developing world? PLoS Med 2(5):e107
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