Abstract
In meta-analysis for diagnostic test accuracy (DTA), summary measures such as sensitivity, specificity, and odds ratio are used. However, these measures may not be adequate to integrate studies with low prevalence, which is why statistical modeling based on true positives and false positives is necessary. In this context, there are several statistical methods, the first of which is a bivariate random effects model, part of the assumption that the logit of sensitivity and specificity follow a bivariate normal distribution, the second, refers to the HSROC or hierarchical model, is similar to bivariate, with the particularity that it directly models the sensitivity and specificity relationship through cut points. Using simulations, we investigate the performance of hierarchical models, varying their parameters and hyperparameters and proposing a better management of variability within and between studies. The results of the simulated data are analyzed according to the criterion of adjustment of the models and estimates of their parameters.
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References
Midgette, A.S., Stukel, T.A., Littenberg, B.: A meta-analytic method for summarizing diagnostic test performances: receiver-operating-characteristic-summary point estimates. Med. Decis. Mak. 13(3), 253–257 (1993)
Moses, L.E., Shapiro, D., Littenberg, B.: Combining independent studies of a diagnostic test into a summary ROC curve: data-analytic approaches and some additional considerations. Stat. Med. 12(14), 1293–1316 (1993)
Reitsma, J.B., Glas, A.S., Rutjes, A.W., Scholten, R.J., Bossuyt, P.M., Zwinderman, A.H.: Bivariate analysis of sensitivity and specificity produces informative summary measures in diagnostic reviews. J. Clin. Epidemiol. 58(10), 982–990 (2005)
Rutter, C.M., Gatsonis, C.A.: A hierarchical regression approach to meta-analysis of diagnostic test accuracy evaluations. Stat. Med. 20(19), 2865–2884 (2001)
Arends, L.R., Hamza, T.H., Van Houwelingen, J.C., Heijenbrok-Kal, M.H., Hunink, M.G.M., Stijnen, T.: Bivariate random effects meta-analysis of ROC curves. Med. Decis. Mak. 28(5), 621–638 (2008)
Harbord, R.M., Deeks, J.J., Egger, M., Whiting, P., Sterne, J.A.: A unification of models for meta-analysis of diagnostic accuracy studies. Biostatistics 8(2), 239–251 (2006)
Kotz, S., Balakrishnan, N., Johnson, N.L.: Bivariate and trivariate normal distributions. Contin. Multivar. Distrib. 1, 251–348 (2000)
Van Houwelingen, H.C., Arends, L.R., Stijnen, T.: Advanced methods in meta-analysis: multivariate approach and meta-regression. Stat. Med. 21(4), 589–624 (2002)
Chu, H., Guo, H., Zhou, Y.: Bivariate random effects meta-analysis of diagnostic studies using generalized linear mixed models. Med. Decis. Mak. 30(4), 499–508 (2010)
Macaskill, P.: Empirical Bayes estimates generated in a hierarchical summary ROC analysis agreed closely with those of a full Bayesian analysis. J. Clin. Epidemiol. 57(9), 925–932 (2004)
Chappell, F.M., Raab, G.M., Wardlaw, J.M.: When are summary ROC curves appropriate for diagnostic meta-analyses? Stat. Med. 28(21), 2653–2668 (2009)
Kriston, L., Hölzel, L., Weiser, A.K., Berner, M.M., Härter, M.: Meta-analysis: are 3 questions enough to detect unhealthy alcohol use? Ann. Intern. Med. 149(12), 879–888 (2008)
Bachmann, L.M., Puhan, M.A., Ter Riet, G., Bossuyt, P.M.: Sample sizes of studies on diagnostic accuracy: literature survey. Bmj 332(7550), 1127–1129 (2006)
Doebler, P., Holling, H.: Meta-analysis of diagnostic accuracy with mada. Reterieved at: https://cran.rproject.org/web/packages/mada/vignettes/mada.pdf (2015)
Dendukuri, N., Schiller, I., Joseph, L., Pai, M.: Bayesian meta-analysis of the accuracy of a test for tuberculous pleuritis in the absence of a gold standard reference. Biometrics 68(4), 1285–1293 (2012)
Grycuk, R., Gabryel, M., Scherer, R., Voloshynovskiy, S.: Multi-layer architecture for storing visual data based on WCF and microsoft SQL server database. In: International Conference on Artificial Intelligence and Soft Computing, pp. 715–726. Springer, Cham (2015)
Sheu, C.F., Chen, C.T., Su, Y.H., Wang, W.C.: Using SAS PROC NLMIXED to fit item response theory models. Behav. Res. Methods 37(2), 202–218 (2005)
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Project name: BivariateHSROC_Simulation
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Project homepage: https://sourceforge.net/projects/metahi/
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Operating system(s): Microsoft Windows, Linux and Mac
Programming language: SAS, License: Open Source and free
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Bauz-Olvera, S.A., Pambabay-Calero, J.J., Nieto-Librero, A.B., Galindo-Villardón, M.P. (2019). Meta-Analysis in DTA with Hierarchical Models Bivariate and HSROC: Simulation Study. In: Antoniano-Villalobos, I., Mena, R., Mendoza, M., Naranjo, L., Nieto-Barajas, L. (eds) Selected Contributions on Statistics and Data Science in Latin America. FNE 2018. Springer Proceedings in Mathematics & Statistics, vol 301. Springer, Cham. https://doi.org/10.1007/978-3-030-31551-1_3
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DOI: https://doi.org/10.1007/978-3-030-31551-1_3
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