References
- H. Akaike, Information theory and an extension of the maximum likelihood principle, Proceedings of 2nd International Symposium on Information Theory, Akadémiai Kiadó, 1973, pp. 267–281.
- L.M. Bachmann, M.A. Puhan, G. Ter Riet, and P.M. Bossuyt, Sample sizes of studies on diagnostic accuracy: Literature survey, BMJ 332 (2006), pp. 1127–1129.
- K.A. Baggerly, Empirical likelihood as a goodness-of-fit measure, Biometrika 85 (1998), pp. 535–547.
- M.L. Bastos, G. Tavaziva, S.K. Abidi, J.R. Campbell, L.P. Haraoui, J.C. Johnston, Z. Lan, S. Law, E. MacLean, A. Trajman, D. Menzies, A. Benedetti, and F. Ahmad Khan, Diagnostic accuracy of serological tests for covid-19: Systematic review and meta-analysis, BMJ 370 (2020), pp. m2516.
- K.P. Burnham and D.R. Anderson, Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach, Springer Science & Business Media, New York, 2003.
- J.E. Cavanaugh, Unifying the derivations for the akaike and corrected akaike information criteria, Stat. Probab. Lett. 33 (1997), pp. 201–208.
- Centers for Disease Control and Prevention and others, Duration of Isolation and Precautions for Adults with Covid-19, CDC, Atlanta.
- F. Chappell, G. Raab, and J. Wardlaw, When are summary roc curves appropriate for diagnostic meta-analyses? Stat. Med. 28 (2009), pp. 2653–2668.
- D. Chen, Y. Zhang, Y. Xu, T. Shen, G. Cheng, B. Huang, X. Ruan, and C. Wang, Comparison of chemiluminescence immunoassay, enzyme-linked immunosorbent assay and passive agglutination for diagnosis of mycoplasma pneumoniae infection, Ther. Clin. Risk Manag. 14 (2018), pp. 1091–1097.
- H. Chu, H. Guo, and Y. Zhou, Bivariate random effects meta-analysis of diagnostic studies using generalized linear mixed models, Med. Decis. Making 30 (2010), pp. 499–508.
- H. Chu, L. Nie, S.R. Cole, and C. Poole, Meta-analysis of diagnostic accuracy studies accounting for disease prevalence: Alternative parameterizations and model selection, Stat. Med. 28 (2009), pp. 2384–2399.
- G. Claeskens and N.L. Hjort, Model Selection and Model Averaging, Cambridge University Press, Cambridge, 2008.
- F. de Ory, T. Minguito, P. Balfagón, and J.C. Sanz, Comparison of chemiluminescent immunoassay and elisa for measles igg and igm, Apmis 123 (2015), pp. 648–651.
- J.J. Decks, Systematic reviews in health care: Systematic reviews of evaluations of diagnostic and screening tests, BMJ 323 (2001), pp. 157–162.
- P. Doebler, H. Holling, and D. Böhning, A mixed model approach to meta-analysis of diagnostic studies with binary test outcome, Psychol. Methods 17 (2012), pp. 418–436.
- J. Fan and J. Lv, A selective overview of variable selection in high dimensional feature space, Stat. Sin. 20 (2010), pp. 101–148.
- C. Gatsonis and P. Paliwal, Meta-analysis of diagnostic and screening test accuracy evaluations: Methodologic primer, Am. J. Roentgenol. 187 (2006), pp. 271–281.
- S. Greven and T. Kneib, On the behaviour of marginal and conditional aic in linear mixed models, Biometrika 97 (2010), pp. 773–789.
- A. Guolo, A double simex approach for bivariate random-effects meta-analysis of diagnostic accuracy studies, BMC Med. Res. Methodol. 17 (2017), pp. 266.
- R.M. Harbord, J.J. Deeks, M. Egger, P. Whiting, and J.A. Sterne, A unification of models for meta-analysis of diagnostic accuracy studies, Biostatistics 8 (2006), pp. 239–251.
- J.P. Higgins and S. Green, Cochrane Handbook for Systematic Reviews of Interventions, vol. 4, John Wiley & Sons, Hoboken, 2011.
- H. Holling, W. Böhning, and D. Böhning, Likelihood-based clustering of meta-analytic sroc curves, Psychometrika 77 (2012), pp. 106–126.
- H. Honest and K.S. Khan, Reporting of measures of accuracy in systematic reviews of diagnostic literature, BMC Health Serv. Res. 2 (2002), p. 112.
- L. Irwig, P. Macaskill, P. Glasziou, and M. Fahey, Meta-analytic methods for diagnostic test accuracy, J. Clin. Epidemiol. 48 (1995), pp. 119–130.
- H. Liang, H. Wu, and G. Zou, A note on conditional aic for linear mixed-effects models, Biometrika 95 (2008), pp. 773–778.
- L.E. Moses, D. Shapiro, and B. Littenberg, Combining independent studies of a diagnostic test into a summary roc curve: Data-analytic approaches and some additional considerations, Stat. Med. 12 (1993), pp. 1293–1316.
- A. Owen, Empirical likelihood ratio confidence regions, Ann. Stat. 18 (1990), pp. 90–120.
- J.B. Reitsma, A.S. Glas, A.W. Rutjes, R.J. Scholten, P.M. Bossuyt, and A.H. Zwinderman, Bivariate analysis of sensitivity and specificity produces informative summary measures in diagnostic reviews, J. Clin. Epidemiol. 58 (2005), pp. 982–990.
- C.M. Rutter and C.A. Gatsonis, A hierarchical regression approach to meta-analysis of diagnostic test accuracy evaluations, Stat. Med. 20 (2001), pp. 2865–2884.
- P. Schlattmann, M. Verba, M. Dewey, and M. Walther, Mixture models in diagnostic meta-analyses–clustering summary receiver operating characteristic curves accounted for heterogeneity and correlation, J. Clin. Epidemiol. 68 (2015), pp. 61–72.
- G. Schwarz, Estimating the dimension of a model, Ann. Statist. 6 (1978), pp. 461–464.
- L. Shamseer, D. Moher, M. Clarke, D. Ghersi, A. Liberati, M. Petticrew, P. Shekelle, and L.A. Stewart, Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015: Elaboration and explanation, BMJ 349 (2015), pp. g7647.
- A. Sotiriadis, S. Papatheodorou, and W. Martins, Synthesizing evidence from diagnostic accuracy tests: The sedate guideline, Ultrasound Obstet. Gynecol. 47 (2016), pp. 386–395.
- T.A. Trikalinos, C.M. Balion, C.I. Coleman, L. Griffith, P.L. Santaguida, B. Vandermeer, and R. Fu, meta-analysis of test performance when there is a ‘gold standard’, J. Gen. Intern. Med. 27 (2012), pp. 56–66.
- G. Tripepi, K.J. Jager, F.W. Dekker, and C. Zoccali, Diagnostic methods 2: Receiver operating characteristic (ROC) curves, Kidney Int. 76 (2009), pp. 252–256.
- F. Vaida and S. Blanchard, Conditional akaike information for mixed-effects models, Biometrika 92 (2005), pp. 351–370.
- N. Verma, D. Patel, and A. Pandya, Emerging diagnostic tools for detection of covid-19 and perspective, Biomed. Microdevices 22 (2020), pp. 1–18.
- S. Walter and A. Jadad, Meta-analysis of screening data: A survey of the literature, Stat. Med. 18 (1999), pp. 3409–3424.
- World Health Organization, WHO coronavirus disease (COVID-19) dashboard. Available at https://covid19.who.int/.