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Meta Regression In Comprehensive Meta Analysis Keygen
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Meta Regression In Comprehensive Meta Analysis Keygen
13 Oct 2021: The PLOS Medicine Staff (2021) Correction: Accuracy of novel antigen rapid diagnostics for SARS-CoV-2: A living systematic review and meta-analysis.PLOS Medicine 18(10): e1003825. View correction
SARS-CoV-2 antigen rapid diagnostic tests (Ag-RDTs) are increasingly being integrated in testing strategies around the world. Studies of the Ag-RDTs have shown variable performance. In this systematic review and meta-analysis, we assessed the clinical accuracy (sensitivity and specificity) of commercially available Ag-RDTs.
Citation: Brümmer LE, Katzenschlager S, Gaeddert M, Erdmann C, Schmitz S, Bota M, et al. (2021) Accuracy of novel antigen rapid diagnostics for SARS-CoV-2: A living systematic review and meta-analysis. PLoS Med 18(8): e1003735.
We differentiated between clinical accuracy studies (performed on clinical samples) and analytical accuracy studies (performed on spiked samples with a known quantity of virus). Analytical accuracy studies can differ widely in methodology, impeding an aggregation of their results. Thus, while we extracted the data for both kinds of studies, we only considered data from clinical accuracy studies as eligible for the meta-analysis. Separately, we summarized the results of analytical studies and compared them with the results of the meta-analysis for individual tests.
Based on 119 datasets with 71,424 tests performed, we were able to perform bivariate meta-analysis of the sensitivity and specificity for 12 different Ag-RDTs (Fig 4). Across these, the pooled estimates of sensitivity and specificity on all samples were 72.1% (95% CI 68.8% to 75.3%) and 99.0% (95% CI 98.7% to 99.2%), respectively, which were very similar to the overall pooled estimates across all meta-analyzed datasets (71.2% and 98.9%, respectively, above).
Three Ag-RDTs did not have sufficient data to allow for a bivariate meta-analysis, so a univariate analysis was conducted (Fig 5). For the INNOVA SARS-CoV-2 Antigen Rapid Qualitative Test by Innova Medical Group (California, US), this resulted in a pooled sensitivity and specificity of 76.1% (95% CI 68.1% to 84.1%) and 99.4% (95% CI 98.7% to 100%), respectively. For the NADAL by nal von minden (Germany) and the COVID-19 Rapid Antigen Visual Read by SureScreen Diagnostics (UK), sufficient data were available to analyze only sensitivity, resulting in pooled sensitivity estimates of 58.4% (95% CI 29.2% to 87.6%) and 58.0% (95% CI 38.3% to 77.6%), respectively.
The remaining 35 Ag-RDTs did not present sufficient data for univariate or bivariate meta-analysis. However, 9/35 had results presented in more than 1 dataset, and these are summarized in Table 2. Herein, the widest ranges of sensitivity were found for the ESPLINE SARS-CoV-2 by Fujirebio (Japan), with sensitivity reported between 8.1% and 80.7%, and the RIDA QUICK SARS-CoV-2 Antigen by R-Biopharm (Germany), with sensitivity between 39.2% and 77.6%, both with 3 datasets each. In contrast, 2 other tests with 2 datasets each showed the least variability in sensitivity: The Zhuhai Encode Medical Engineering SARS-CoV-2 Antigen Rapid Test (China) reported sensitivity between 74.0% and 74.4%, and the COVID-19 Rapid Antigen Fluorescent by SureScreen Diagnostics (UK) reported sensitivity between 60.3% and 69.0%. However, for both tests, both datasets originated from the same studies. Overall, the lowest sensitivity range was reported for the SARS-CoV-2 Antigen Rapid Test by MEDsan (Germany): 36.5% to 45.2% across 2 datasets. The specificity ranges were above 96% for most of the tests. A notable outlier was the 2019-nCov Antigen Rapid Test Kit by Shenzhen Bioeasy Biotechnology (China; henceforth called Bioeasy), reporting the worst, with a specificity as low as 85.6% in 1 study. Forest plots for the datasets for each Ag-RDT are provided in S3 Fig. The remaining 26 Ag-RDTs that were evaluated in 1 dataset only are included in Table 1 S3 Fig.
All other head-to-head comparisons were not IFU-conforming. In one of these, the Rapid COVID-19 Ag Test by Healgen (sensitivity 77.1%) performed better than Standard Q and Panbio (sensitivity 69.8% and 67.7%, respectively) [178]. In contrast to the overall findings of the meta-analysis above, 2 other head-to-head studies found that both Standard Q (sensitivity 43.6% and 49.4%) and Panbio (sensitivity 38.6% and 44.6%) had lower performance than the CLINITEST Rapid COVID-19 Antigen Test by Siemens Healthineers (Germany; henceforth called Clinitest), with reported sensitivity of 51.5% and 54.9% [167,279]. However, another study found both Standard Q and Panbio (sensitivity 81.0% and 82.9%, respectively) to have a higher accuracy than Sofia (sensitivity 80.4%) [196].
We were not able to perform a subgroup meta-analysis for BAL/TW due to insufficient data: There was only 1 study with 73 samples evaluating Rapigen, Panbio, and Standard Q [286]. However, BAL/TW would in any case be considered an off-label use.
Within the datasets possible to meta-analyze, 17,964 (54.1%) samples were from symptomatic, and 15,228 (45.9%) from asymptomatic, patients. The pooled sensitivity for symptomatic patients was markedly different from that of asymptomatic patients: 76.7% (95% CI 70.6% to 81.9%) versus 52.5% (95% CI 43.7% to 61.1%). Specificity was 99% for both groups (Fig 9). Median Ct values differed in symptomatic and asymptomatic patients. For those studies where it was possible to extract a median Ct value, it ranged from 20.5 to 27.0 in symptomatic patients [170,207,226,258,271,272] and from 27.2 to 30.5 in asymptomatic patients [170,201,258].
Overall, the reported analytical sensitivity (limit of detection [LOD]) in the studies resembled the results of the meta-analysis presented above. Rapigen (LOD, in log10 copies per swab: 10.2) and Coris (LOD 7.46) were found to perform worse than Panbio (LOD 6.6 to 6.1) and Standard Q (LOD 6.8 to 6.0), whereas Clinitest (LOD 6.0) and BinaxNOW by Abbott (LOD 4.6 to 4.9) performed better [191,256,282]. Similar results were found in another study, where Standard Q showed the lowest LOD (detecting virus up to what is an equivalent Ct value of 26.3 to 28.7), compared to that of Rapigen and Coris (detecting virus up to what is an equivalent Ct value of only 18.4 for both) [208,274,275]. However, another study found Panbio, Standard Q, Coris, and BinaxNOW to have a similar LOD values of 5.0 103 plaque forming units (PFU)/mL, but the ESPLINE SARS-CoV-2 by Fujirebio (Japan), the COVID-19 Rapid Antigen Test by Mologic (UK), and the Sure Status COVID-19 Antigen Card Test by Premier Medical Corporation (India) performed markedly better (LOD 2.5 102 to 5.0 102 PFU/mL) [173]. An overview of all LOD values reported in the studies can be found in S3 Table.
The main strengths of our study lie in its comprehensive approach and continuous updates. By linking this review to our website, , we strive to equip decision makers with the latest research findings on Ag-RDTs for SARS-CoV-2 and, to the best of our knowledge, are the first in doing so. At least once per week the website is updated by continuing the literature search and process described above. We plan to update the meta-analysis on a monthly basis and publish it on the website. Furthermore, our study used rigorous methods as both the study selection and data extraction were performed by one author and independently validated by a second, we conducted blinded pilot extractions before of the actual data extraction, and we prepared a detailed interpretation guide for the QUADAS-2 tool.
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MIX 2.0 Pro is the professional version of the MIX 2.0 software and can be used to perform meta-analysis in Excel. MIX 2.0 Pro creates a user-friendly interface inside your Excel and super-charges the power of Excel to perform a meta-analysis. Currently, MIX 2.0 focuses on single-group or pairwise comparison meta-analysis, providing users with extensive exploration, synthesis, and evaluation toolsets. Follow this link for a detailed list of features.
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The meta-regression features are a recent addition and are added step by step. Initially, we kept this out of this software, because we felt that meta-regression should be done with statistical software that has more flexibility in terms of tweaking and checking regression methods and results. Due to overwhelming requests for meta-regression, we have decided to integrate univariable regression methods into MIX 2.0, as of version 2.015 (end of 2016). We have focused on fixed-effect models and will explore ways to expand the interface and analysis options in the future. 153554b96e