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. 2015 Nov 4;14:434. doi: 10.1186/s12936-015-0966-y

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© Valle et al. 2015

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.

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Fig. 2 — The Bayesian models outperformed the standard logistic regression model based on the MSE criterion. Mean squared error (MSE) for four different methods are shown for different scenarios of sensitivity (SN) and specificity (SP) [SN = 0.6 and SP = 0.9 (upper left panel); SN = 0.9 and SP = 0.9 (upper right panel); SN = 0.6 and SP = 0.98 (lower left panel); SN = 0.9 and SP = 0.98 (lower right panel)]. These results are based on 100 simulated datasets, with 2000 individuals in each dataset. Smaller values indicate better performance