4.  Draw the number of diseased $n_{1}$ from a $B(n,0.4)$ distribution and set $n_0=n-n_1$ .

5.  For each test $t$ in $1,\ldots,T$ generate $y_{11t}$ and $y_{00t}$ from a $B(n_1,x_{1t})$ and $B(n_0,x_{0t})$ distribution, respectively, and set $y_{01t}=n_1-y_{11t},y_{10t}=n_0-y_{00t}.$

Representative summaries of findings on the performance of the ML method in Section \[computation\] are given in Table \[sim1\] for 6-dimensional ($T = 3$) one-factor copula models with normal and beta margins. The true (simulated) copula distributions are the Clayton and the Clayton copula rotated by $270^\circ$ to handle the positive and negative dependencies, respectively. True sensitivity $\bfpi_1$ and specificity $\bfpi_2$ vectors are $(0.8,0.7,0.8)$ and $(0.7,0.8,0.7)$, the variability parameter vectors are $\debf_1=\debf_2=(1,1,1)$ or $\debf_1=\debf_2=(0.1,0.1,0.1)$ for normal or beta margin, respectively, and the Kendall’s $\taubf=(0.6,0.7,0.5,-0.3,-0.4,-0.2)$. Under each margin, 10,000 meta-analysis data sets are simulated with $N=50$ studies in each data set.

The table contains the resultant biases, root mean square errors (RMSEs) and standard deviations (SDs), along with average theoretical variances, scaled by 100, for the MLEs of the main parameters of interest under different copula choices and margins. The theoretical variances of the MLEs are obtained via the gradients and the Hessian that were computed numerically during the maximization process. The results for the 