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Efforts to improve the Hamilton Rating Scale for Depression (HRSD) have included shortening the scale by selecting the best performing items, lengthening the scale by assessing additional symptoms, modifying the format and scoring of existing items, and developing structured interview guides for administration. We defined item performance exclusively in terms of the ability of items to discriminate differences among levels of depressive severity which has not be used to guide any revisions of the HRSD conducted to date. Two techniques derived from item response theory were used to improve the ability of the HRSD to discriminate among individuals with different degrees of depressive severity. Item response curves were used to quantify the ability of items to discriminate among individual differences in depressive severity, on the basis of which the most discriminating items were selected. Maximum likelihood estimates were used to compute an optimal depressive severity score, using all items, but which weighted highly discriminating items more so than items that did not discriminate well. The utility of each method was evaluated by comparing a subset of optimally discriminating items and maximum likelihood estimates of depressive severity to the Maier Philipp subscale of the HRSD, in terms of how well scales discriminate treatment effects. Effect sizes for overall change in depression severity as well as effect sizes differentiating response to treatment versus placebo were evaluated in a sample of 491 patients receiving fluoxetine and 494 patients receiving placebo. Results of analyses identified a new subset of items (IRT-6), selected on the basis of their ability to discriminate among differences in depressive severity, that accounted for more variance in full-scale HRSD scores and was better at detecting change in illness severity than the Maier Philipp subscale of the HRSD. The IRT-6 subscale was equally good as the Maier Philipp subscale in differentiating treatment from placebo response. No evidence supporting the benefits of using maximum likelihood estimates to develop optimally performing subscales was found. Implications of the results are discussed in terms of strategies for optimizing the assessment of change in overall depression severity as well as differentiating treatment response.