Development and validation of study filters for identifying controlled non-randomized studies in PubMed and Ovid MEDLINE
- PMID: 32472632
- DOI: 10.1002/jrsm.1425
Development and validation of study filters for identifying controlled non-randomized studies in PubMed and Ovid MEDLINE
Abstract
A retrospective analysis published by the German Institute for Quality and Efficiency in Health Care (IQWiG) in 2018 concluded that no filter for non-randomized studies (NRS) achieved sufficient sensitivity (≥92%), a precondition for comprehensive information retrieval. New NRS filters are therefore required, taking into account the challenges related to this study type. Our evaluation focused on the development of study filters for NRS with a control group ("controlled NRS"), as this study type allows the calculation of an effect size. In addition, we assumed that due to the more explicit search syntax, controlled NRS are easier to identify than non-controlled ones, potentially resulting in better performance measures of study filters for controlled NRS. Our aim was to develop study filters for identifying controlled NRS in PubMed and Ovid MEDLINE. We developed two new search filters that can assist clinicians and researchers in identifying controlled NRS in PubMed and Ovid MEDLINE. The reference set was based on 2110 publications in Medline extracted from 271 Cochrane reviews and on 4333 irrelevant references. The first filter maximizes sensitivity (92.42%; specificity 79.67%, precision 68.49%) and should be used when a comprehensive search is needed. The second filter maximizes specificity (92.06%; precision 82.98%, sensitivity 80.94%) and should be used when a more focused search is sufficient.
Keywords: MEDLINE; Non-randomized controlled trials as topic; bibliographic databases; information storage and retrieval; review literature as topic.
© 2020 The Authors. Research Synthesis Methods published by John Wiley & Sons Ltd.
References
REFERENCES
-
- Deeks JJ, Dinnes J, D'Amico R, et al. Evaluating non-randomised intervention studies. Health Technol Assess. 2003;7(27):1-173.
-
- Hausner E, Metzendorf MI, Richter B, Lotz F, Waffenschmidt S. Study filters for non-randomized studies of interventions consistently lacked sensitivity upon external validation. BMC Med Res Methodol. 2018;18(1):171.
-
- Glanville J, Eyers J, Jones AM, et al. Quasi-experimental study designs series; paper 8: identifying quasi-experimental studies to inform systematic reviews. J Clin Epidemiol. 2017;89:67-76.
-
- Fraser C, Murray A, Burr J. Identifying observational studies of surgical interventions in MEDLINE and EMBASE: a validation of a search filter. BMC Med Res Methodol. 2006;6:41.
-
- Li L, Smith HE, Atun R, Tudor Car L. Search strategies to identify observational studies in MEDLINE and Embase. Cochrane Database Syst Rev. 2019;(3):MR000041.
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