Meta-regression, the use of regression methods to incorporate the effect of covarying factors on summary measures of performance, has been used to explore between-study heterogeneity in therapeutic studies (38). In diagnostic studies, likewise, heterogeneity in sensitivity and specificity can result from many causes related to definitions of the test and reference standards, operating characteristics of the test, methods of data collection, and patient characteristics. Covariates may be introduced into a regression with any test performance measure as the dependent variable. As with any meta-regression, however, the sample size will correspond to the number of studies in the analysis. A small number of studies will limit the power of regression to detect significant effects. As always, we should not assume that the lack of significance implies that no factors could influence the relationship between sensitivity and specificity. Although multivariate meta-regression has advantages, study characteristics are often strongly associated with each other; this leads to collinearity, which creates difficulty in interpreting meta-regression models. Warning signs of collinearity include large pairwise correlations between predictor variables, large changes in coefficients caused by the addition or deletion of other variables, and extremely large standard errors for coefficients.