Interpreting Effect Sizes in Contrast Analysis

Sensitivity and specificity summarize the performance of a diagnostic test with a positive/negative outcome determined by a gold standard. When the test is quantitative, receiver operating characteristic (ROC) curves are used to display the performance of all possible cutpoints of the quantitative diagnostic marker. The ROC curve offers a graphical interpretation of the trade-off between sensitivity and specificity of the range of possible cutpoints. Various methods are used to estimate the ROC curve including empirical, parametric, semiparametric, and regression methods. Similarly, various software packages provide ROC curve estimation. Regression methods based on using generalized linear models are discussed as well as their implementation with the SAS procedures PROC LOGISTIC and PROC GENMOD. Recent attention has been given to determining the optimal decision rule, also called the optimal operating point (OOP). Similar to the ROC curve, the OOP provides a graphical interpretation for decision making. Three methods are presented for determination of the OOP and interpretations of the advantage and disadvantage of each are discussed. An example for prediction of remission from a psychological study on depression is illustrated.