The proposed project is an individual patient data meta-analysis. Data will be collated from 31 trials of second-generation antidepressants for the treatment of major depressive disorder, using data from trial arms treated with placebo or Food and Drug Administration (FDA)-approved antidepressants. These trials include a total of approximately 7,800 antidepressant-treated and 3,000 placebo-treated participants. Penalized regression methods (specifically least absolute shrinkage and selection operator, 'lasso') will be used to examine the relationship between early improvement in specific depressive symptoms and response to treatment. Furthermore, the investigators will examine whether interactions among early-improving symptoms and between early-improving symptoms and demographic variables such as age and gender improve the prediction of treatment outcome. Finally, the investigators will also examine whether the prediction of response to treatment by early improvement in specific symptoms is dependent upon the type of treatment provided (placebo, selective serotonin reuptake inhibitors [SSRIs] or serotonin-norepinephrine reuptake inhibitors [SNRIs]), which would suggest a drug-specific mechanism.