To plot a Summary Receiver Operating Characteristic (SROC) curve for your meta-analysis using a bivariate random effects logistic regression model, you will need to follow several steps. Since you're using WinBUGS for your analysis, you'll need to extract the necessary parameters from your model's output and then use a statistical software or programming language like R or Python to plot the curve. Here's a general guide to follow:

1. Extract Model Output from WinBUGS

Parameter Estimates: After running your bivariate random effects logistic regression model in WinBUGS, extract the parameter estimates. These would typically include estimates for the logit of sensitivity and specificity, and their corresponding variances and covariances.

2. Compute Point Estimates and Confidence Intervals

Sensitivity and Specificity: Convert the logit estimates of sensitivity and specificity back to the probability scale. This can be done using the logistic transformation.

Variance-Covariance Matrix: Use the variance and covariance estimates to construct a variance-covariance matrix for the sensitivity and specificity estimates.

3. Plotting the SROC Curve

Choose a Software/Tool: While WinBUGS is not designed for plotting, you can use R or Python. Both have packages/functions for creating SROC curves.

Data Preparation: Prepare your data (sensitivity, specificity, and their variances/covariances) in a format suitable for the chosen software.

@Amani Al-Rubkhi