A critical challenge in the management of major depressive disorder (MDD) is the selection of treatment for each individual patient. Although treatments with depression can restore people's lives, with any treatment modality there are some individuals who do not achieve complete remission of symptoms, whether the intervention is pharmacological, psychological, or somatic. While predictors for some treatments have been proposed for groups of patients, the translation of these predictors to individualized patient care has remained elusive. In an analysis of data from the NCT 00104611 multi-site, randomized, sham-controlled trial of TMS, it was found that a larger number of prior treatment failures, longer duration of the current episode, and the presence of comorbid anxiety were individual patient characteristics associated with poorer acute outcomes with TMS treatment in the randomized period (Lisanby et al., in press). This publication did not report standard predictor metrics (e.g., sensitivity, specificity, positive- or negative-predictive accuracy, ROC curves), so it is difficult to assess the value of these clinical factors in treatment planning for individual patients. A predictor that could distinguish between individuals likely to remit with TMS versus those likely to need a different intervention would be of great use to clinicians and patients in making treatment decisions.