Comparing risk prediction models

Centre for Statistics in Medicine, Wolfson College Annexe, University of Oxford, Oxford OX2 6UD, UK.
BMJ (online) (Impact Factor: 16.38). 05/2012; 344:e3186. DOI: 10.1136/bmj.e3186
Source: PubMed
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    ABSTRACT: Antibodies to prothrombin can be detected by ELISA using prothrombin coated onto irradiated plates (aPT) or the phosphatidylserine/prothrombin complex as antigen (aPS/PT) and they have been both related with the clinical manifestation of APS. Current evidence supports the concept that they belong to distinct populations of autoantibodies. Nevertheless, they can both be detected simultaneously in one patient. This mini-review will focus on data available on aPS/PT antibodies and their clinical utility in the diagnosis of APS.
    Lupus 10/2014; 23(12):1309-12. DOI:10.1177/0961203314538332 · 2.48 Impact Factor
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    ABSTRACT: Carl Moons and colleagues provide a checklist and background explanation for critically appraising and extracting data from systematic reviews of prognostic and diagnostic prediction modelling studies. Please see later in the article for the Editors' Summary.
    PLoS Medicine 10/2014; 11(10):e1001744. DOI:10.1371/journal.pmed.1001744 · 15.25 Impact Factor
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    ABSTRACT: Background Prediction models are developed to aid healthcare providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision-making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed.Materials and methodsThe Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) initiative developed a set of recommendations for the reporting of studies developing, validating or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed. An extensive list of items based on a review of the literature was created, which was reduced after a Web-based survey and revised during a 3-day meeting in June 2011 with methodologists, healthcare professionals and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors.ResultsThe resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document.Conclusions To aid the editorial process and readers of prediction model studies, it is recommended that authors include a completed checklist in their submission (also available at
    European Journal of Clinical Investigation 01/2015; 32(2). DOI:10.1111/eci.12376 · 3.37 Impact Factor

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