imply that the sensitivity and specificity of each test have tail dependence. This is due the fact that the one-factor copula can be explained as a truncated Canonical vine rooted at the latent variable . That is the 1-factor copula has bivariate copulas in the 1st level of the vine and independence copulas in all the remaining levels of the vine (truncated after the 1st level). [@joeetal10] show that in order for a vine copula to have tail dependence for all bivariate margins, it is only necessary for the bivariate copulas in level 1 to have (tail) dependence and it is not necessary for the conditional bivariate copulas in levels $2,\ldots,2T-1$ to have tail dependence. Hence, the tail dependence between the factor and each of the latent sensitivities/specificities is inherited to the tail dependence between the latent sensitivities and specificities. Note also that in line with the results in the preceding subsection, the biases of the estimates increase when the assumed bivariate copulas have tail dependence of opposite direction from the true copulas or tail independence. When the BVN copulas with intermediate tail dependence are used to link the factor with the latent sensitivities/specificities, the estimates are robust to misspecification 