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      PROBAST: A Tool to Assess the Risk of Bias and Applicability of Prediction Model Studies

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          Abstract

          Clinical prediction models combine multiple predictors to estimate risk for the presence of a particular condition (diagnostic models) or the occurrence of a certain event in the future (prognostic models). PROBAST (Prediction model Risk Of Bias ASsessment Tool), a tool for assessing the risk of bias (ROB) and applicability of diagnostic and prognostic prediction model studies, was developed by a steering group that considered existing ROB tools and reporting guidelines. The tool was informed by a Delphi procedure involving 38 experts and was refined through piloting. PROBAST is organized into the following 4 domains: participants, predictors, outcome, and analysis. These domains contain a total of 20 signaling questions to facilitate structured judgment of ROB, which was defined to occur when shortcomings in study design, conduct, or analysis lead to systematically distorted estimates of model predictive performance. PROBAST enables a focused and transparent approach to assessing the ROB and applicability of studies that develop, validate, or update prediction models for individualized predictions. Although PROBAST was designed for systematic reviews, it can be used more generally in critical appraisal of prediction model studies. Potential users include organizations supporting decision making, researchers and clinicians who are interested in evidence-based medicine or involved in guideline development, journal editors, and manuscript reviewers.

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          Most cited references 36

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          QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies.

          In 2003, the QUADAS tool for systematic reviews of diagnostic accuracy studies was developed. Experience, anecdotal reports, and feedback suggested areas for improvement; therefore, QUADAS-2 was developed. This tool comprises 4 domains: patient selection, index test, reference standard, and flow and timing. Each domain is assessed in terms of risk of bias, and the first 3 domains are also assessed in terms of concerns regarding applicability. Signalling questions are included to help judge risk of bias. The QUADAS-2 tool is applied in 4 phases: summarize the review question, tailor the tool and produce review-specific guidance, construct a flow diagram for the primary study, and judge bias and applicability. This tool will allow for more transparent rating of bias and applicability of primary diagnostic accuracy studies.
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            Is Open Access

            ROBINS-I: a tool for assessing risk of bias in non-randomised studies of interventions

            Non-randomised studies of the effects of interventions are critical to many areas of healthcare evaluation, but their results may be biased. It is therefore important to understand and appraise their strengths and weaknesses. We developed ROBINS-I (“Risk Of Bias In Non-randomised Studies - of Interventions”), a new tool for evaluating risk of bias in estimates of the comparative effectiveness (harm or benefit) of interventions from studies that did not use randomisation to allocate units (individuals or clusters of individuals) to comparison groups. The tool will be particularly useful to those undertaking systematic reviews that include non-randomised studies.
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              2013 ACC/AHA Guideline on the Assessment of Cardiovascular Risk

              Supplemental Digital Content is available in the text.
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                Author and article information

                Journal
                Annals of Internal Medicine
                Ann Intern Med
                American College of Physicians
                0003-4819
                January 01 2019
                January 01 2019
                : 170
                : 1
                : 51
                Affiliations
                [1 ]Kleijnen Systematic Reviews, York, United Kingdom (R.F.W., M.W.)
                [2 ]Julius Center for Health Sciences and Primary Care and Cochrane Netherlands, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands (K.G.M., J.B.R.)
                [3 ]Centre for Prognosis Research, Research Institute for Primary Care and Health Sciences, Keele University, Keele, United Kingdom (R.D.R.)
                [4 ]Medical School of the University of Bristol and National Institute for Health Research Collaboration for Leadership in Applied Health Research and Care West, University Hospitals Bristol National Health Service Foundation Trust, Bristol, United Kingdom (P.F.W.)
                [5 ]Centre for Statistics in Medicine, University of Oxford, Oxford, United Kingdom (G.S.C.)
                [6 ]Kleijnen Systematic Reviews, York, United Kingdom, and School for Public Health and Primary Care, Maastricht University, Maastricht, the Netherlands (J.K.)
                [7 ]Institute of Applied Health Research, National Institute for Health Research Birmingham Biomedical Research Centre, College of Medical and Dental Sciences, University of Birmingham, Birmingham, United Kingdom (S.M.)
                Article
                10.7326/M18-1376
                30596875
                © 2019
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