<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Victoria N Nyaga</style></author><author><style face="normal" font="default" size="100%">Aerts, Marc</style></author><author><style face="normal" font="default" size="100%">M. Arbyn</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">ANOVA model for network meta-analysis of diagnostic test accuracy data.</style></title><secondary-title><style face="normal" font="default" size="100%">Stat Methods Med Res</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Stat Methods Med Res</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2016 Sep 20</style></date></pub-dates></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Procedures combining and summarising direct and indirect evidence from independent studies assessing the diagnostic accuracy of different tests for the same disease are referred to network meta-analysis. Network meta-analysis provides a unified inference framework and uses the data more efficiently. Nonetheless, handling the inherent correlation between sensitivity and specificity continues to be a statistical challenge. We developed an arm-based hierarchical model which expresses the logit transformed sensitivity and specificity as the sum of fixed effects for test, correlated study-effects to model the inherent correlation between sensitivity and specificity and a random error associated with various tests evaluated in a given study. We present the accuracy of 11 tests used to triage women with minor cervical lesions to detect cervical precancer. Finally, we compare the results with those from a contrast-based model which expresses the linear predictor as a contrast to a comparator test. The proposed arm-based model is more appealing than the contrast-based model since the former permits more straightforward interpretation of the parameters, makes use of all available data yielding shorter credible intervals, and models more natural variance-covariance matrix structures.&lt;/p&gt;</style></abstract><custom1><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/pubmed/27655805?dopt=Abstract</style></custom1></record></records></xml>