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      Development of an interactive web-based tool to conduct and interrogate meta-analysis of diagnostic test accuracy studies: MetaDTA

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          Abstract

          Background

          Recommended statistical methods for meta-analysis of diagnostic test accuracy studies require relatively complex bivariate statistical models which can be a barrier for non-statisticians. A further barrier exists in the software options available for fitting such models. Software accessible to non-statisticians, such as RevMan, does not support the fitting of bivariate models thus users must seek statistical support to use R, Stata or SAS. Recent advances in web technologies make analysis tool creation much simpler than previously. As well as accessibility, online tools can allow tailored interactivity not found in other packages allowing multiple perspectives of data to be displayed and information to be tailored to the user’s preference from a simple interface. We set out to: (i) Develop a freely available web-based “point and click” interactive tool which allows users to input their DTA study data and conduct meta-analyses for DTA reviews, including sensitivity analyses. (ii) Illustrate the features and benefits of the interactive application using an existing DTA meta-analysis for detecting dementia.

          Methods

          To create our online freely available interactive application we used the existing R packages lme4 and Shiny to analyse the data and create an interactive user interface respectively.

          Results

          MetaDTA, an interactive online application was created for conducting meta-analysis of DTA studies. The user interface was designed to be easy to navigate having different tabs for different functions. Features include the ability for users to enter their own data, customise plots, incorporate quality assessment results and quickly conduct sensitivity analyses. All plots produced can be exported as either .png or .pdf files to be included in report documents. All tables can be exported as .csv files.

          Conclusions

          MetaDTA, is a freely available interactive online application which meta-analyses DTA studies, plots the summary ROC curve, incorporates quality assessment results and allows for sensitivity analyses to be conducted in a timely manner. Due to the rich feature-set and user-friendliness of the software it should appeal to a wide audience including those without specialist statistical knowledge. We encourage others to create similar applications for specialist analysis methods to encourage broader uptake which in-turn could improve research quality.

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          Most cited references21

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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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            Bivariate analysis of sensitivity and specificity produces informative summary measures in diagnostic reviews.

            Studies of diagnostic accuracy most often report pairs of sensitivity and specificity. We demonstrate the advantage of using bivariate meta-regression models to analyze such data. We discuss the methodology of both the summary Receiver Operating Characteristic (sROC) and the bivariate approach by reanalyzing the data of a published meta-analysis. The sROC approach is the standard method for meta-analyzing diagnostic studies reporting pairs of sensitivity and specificity. This method uses the diagnostic odds ratio as the main outcome measure, which removes the effect of a possible threshold but at the same time loses relevant clinical information about test performance. The bivariate approach preserves the two-dimensional nature of the original data. Pairs of sensitivity and specificity are jointly analyzed, incorporating any correlation that might exist between these two measures using a random effects approach. Explanatory variables can be added to the bivariate model and lead to separate effects on sensitivity and specificity, rather than a net effect on the odds ratio scale as in the sROC approach. The statistical properties of the bivariate model are sound and flexible. The bivariate model can be seen as an improvement and extension of the traditional sROC approach.
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              Combining independent studies of a diagnostic test into a summary ROC curve: data-analytic approaches and some additional considerations.

              We consider how to combine several independent studies of the same diagnostic test, where each study reports an estimated false positive rate (FPR) and an estimated true positive rate (TPR). We propose constructing a summary receiver operating characteristic (ROC) curve by the following steps. (i) Convert each FPR to its logistic transform U and each TPR to its logistic transform V after increasing each observed frequency by adding 1/2. (ii) For each study calculate D = V - U, which is the log odds ratio of TPR and FPR, and S = V + U, an implied function of test threshold; then plot each study's point (Si, Di). (iii) Fit a robust-resistant regression line to these points (or an equally weighted least-squares regression line), with V - U as the dependent variable. (iv) Back-transform the line to ROC space. To avoid model-dependent extrapolation from irrelevant regions of ROC space we propose defining a priori a value of FPR so large that the test simply would not be used at that FPR, and a value of TPR so low that the test would not be used at that TPR. Then (a) only data points lying in the thus defined north-west rectangle of the unit square are used in the data analysis, and (b) the estimated summary ROC is depicted only within that subregion of the unit square. We illustrate the methods using simulated and real data sets, and we point to ways of comparing different tests and of taking into account the effects of covariates.
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                Author and article information

                Contributors
                suzanne.freeman@leicester.ac.uk
                clareece.kerby@nhs.net
                ap666@le.ac.uk
                njc21@le.ac.uk
                terry.quinn@glasgow.ac.uk
                ajs22@le.ac.uk
                Journal
                BMC Med Res Methodol
                BMC Med Res Methodol
                BMC Medical Research Methodology
                BioMed Central (London )
                1471-2288
                18 April 2019
                18 April 2019
                2019
                : 19
                : 81
                Affiliations
                [1 ]ISNI 0000 0001 2193 314X, GRID grid.8756.c, NIHR Complex Reviews Support Unit, , University of Leicester & University of Glasgow, ; Glasgow, UK
                [2 ]ISNI 0000 0004 1936 8411, GRID grid.9918.9, Biostatistics Research Group, Department of Health Sciences, , University of Leicester, ; Leicester, LE1 7RH UK
                [3 ]ISNI 0000 0001 2193 314X, GRID grid.8756.c, Institute of Cardiovascular and Medical Sciences, , University of Glasgow, ; Glasgow, G12 8QQ UK
                [4 ]Cochrane Dementia and Cognitive Improvement Group, Oxford, UK
                Author information
                http://orcid.org/0000-0001-8045-4405
                Article
                724
                10.1186/s12874-019-0724-x
                6471890
                30999861
                8d9e452a-7b32-4d4b-88aa-658284995347
                © The Author(s). 2019

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 8 November 2018
                : 31 March 2019
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100000272, National Institute for Health Research;
                Award ID: 14/178/29
                Award ID: 14/178/29
                Award ID: 14/178/29
                Award ID: 14/178/29
                Award Recipient :
                Categories
                Software
                Custom metadata
                © The Author(s) 2019

                Medicine
                diagnostic test accuracy,meta-analysis,application
                Medicine
                diagnostic test accuracy, meta-analysis, application

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