Meta-analysis of diagnostic accuracy studies may be performed to provide a summary measure of diagnostic accuracy based on a collection of studies and their reported empirical or estimated smooth ROC curves. Statistical methodology for meta-analysis of diagnostic accuracy studies has largely been focused on the most common type of studies—those reporting estimates of test sensitivity and specificity. To meta-analyze studies with results in more than two categories, one approach is to dichotomize results by grouping them into two categories and then employing one of such methods. However, it is more efficient to take all thresholds into account. Existing methods require the same number and set of categories/thresholds, are computationally intensive adapations of the binary methods or are only implementable using bayesian inference. This talk presents a robust and flexible parametric algorithm which is invariant to the number/set of categories and implementable with standard statistical software such as Stata, SPSS or SAS. The method consists of (1) estimation of study-specific ROC and location-scale parameters by heteroscedastic ordinal(probit or logit) regression; (2) Esimation of correlated or uncorrelated mean location and scale from study-specific estimates using linear mixed modeling by ML, REML or method of moments; and (3) Estimation of Summary ROC (bilogistic versus binormal) and ROC functionals using mean location and scale estimates from (2). The method is illustrated with two data sets (one with studies reporting same set of categories and the other with disparately categorized outcomes). Steps 1 and 2 are performed with oglm(authored by David Williams) and mvmeta (authored by Ian White) respectively. The proposed meta-analytical algorithm may be implemented in Stata using the midacat module.