Background: Systematic reviews are a commonly used research design in the medical field to synthesize study findings. At present-although several systematic reviews of patient preference studies are published-there is no clear guidance available for researchers to conduct this type of systematic review. The aim of our study was to learn the most current practice of conducting these systematic reviews by conducting a survey of the literature regarding reviews of quantitative patient preference studies.
Methods: Our survey included systematic reviews of studies that used a stated quantitative preference design to elicit patient preferences. We identified eligible reviews through a search of the PubMed database. Two investigators with knowledge of the design of patient preference studies independently screened the titles and abstracts, and where needed, screened the full-text of the reviews to determine eligibility. We developed and pilot-tested a form to extract data on the methods used in each systematic review.
Results: Our search and screening identified 29 eligible reviews. A large proportion of the reviews (19/29, 66%) were published in 2014 or after; among them, nine reviews were published in 2016. The median number of databases searched for preference studies was four (interquartile range = 2 to 7). We found that less than half of the reviews (13/29, 45%) clearly reported assessing risk of bias or the methodological quality of the included preference studies; not a single review was able to perform quantitative synthesis (meta-analysis) of the data on patient preferences.
Conclusion: These results suggest that several methodological issues of performing systematic reviews of patient preferences are not yet fully addressed by research and that the methodology may require future development.
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http://dx.doi.org/10.1186/s12874-017-0448-8 | DOI Listing |
BMC Med
January 2025
Institute for Evidence in Medicine, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
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Methods: MEDLINE, Epistemonikos, and the Cochrane Database of Systematic Reviews were searched from January 2010 to September 2021.
Syst Rev
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Conservative Dentistry Department, Faculty of Dentistry, Mansoura University, Mansoura, Postal Code, 35516, Egypt.
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View Article and Find Full Text PDFBMC Cancer
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School of Medicine, IMU University (Formerly Known as the International Medical University), Kuala Lumpur, Malaysia.
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View Article and Find Full Text PDFBMC Med Inform Decis Mak
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Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW, 2007, Australia.
This study aimed to compare and evaluate the prediction accuracy and risk of bias (ROB) of post-traumatic stress disorder (PTSD) predictive models. We conducted a systematic review and random-effect meta-analysis summarizing predictive model development and validation studies using machine learning in diverse samples to predict PTSD. Model performances were pooled using the area under the curve (AUC) with a 95% confidence interval (CI).
View Article and Find Full Text PDFBMC Psychiatry
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