We determined the most appropriate link function for the bivariate model using the deviance information criterion (DIC, Verde 2010) and graphical assessment of model fit.
Given these observations, we only report results from the bivariate model using the complementary log-log, and not from the ROC-based analysis.
All the other parameters, E and σULT, showed no significant differences for both bivariate median regression models, which suggests that age and gender effects can be confounded in Young's modulus and ultimate stress prediction by the bivariate models used.
We fitted the bivariate random effects models using the NLMIXED (non-linear mixed effect) procedure of SAS version 9.2 (SAS Institute, Cary, NC, USA).
*n = 26, † Bivariate model using WHO functional class and hyperbilirubinemia, ‡ Bivariate model using BNP and bilirubin concentration.
For dichotomous test data, analyses were attempted with a bivariate model (using 'metandi' in STATA10 ).
Information theoretic model comparisons showed marginal support for a bivariate model using both respiratory droplet and direct contact transmission data (Table 1).
Instead of using the diagnostic odds ratio, as used in conventional diagnostic meta-analysis[ 12], the bivariate model uses pairs of sensitivity and specificity as the starting point of the analysis.
We applied a weighted bivariate animal model using the DGV, computed as described above for each genotyped animal, and their DEBV to estimate variance and covariance components for each of the studied traits in each breed.
The bivariate model uses a random effects approach for both sensitivity and specificity, allowing for heterogeneity beyond chance due to clinical or methodological differences between studies.
