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JAMA Psychiatry. 2017 Apr 1;74(4):370-378. doi: 10.1001/jamapsychiatry.2017.0025.

Reevaluating the Efficacy and Predictability of Antidepressant Treatments: A Symptom Clustering Approach.

Author information

Department of Psychology, Yale University, New Haven, Connecticut2Spring Health, New York City, New York3Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, Connecticut.
Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut.
Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, Connecticut5Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut6Department of Health Policy and Management, Yale School of Public Health, New Haven, Connecticut.
Department of Psychiatry, University of Texas-Southwestern Medical School, Dallas.
Department of Psychiatry, Yale University, New Haven, Connecticut.
Department of Psychology, Yale University, New Haven, Connecticut.



Depressive severity is typically measured according to total scores on questionnaires that include a diverse range of symptoms despite convincing evidence that depression is not a unitary construct. When evaluated according to aggregate measurements, treatment efficacy is generally modest and differences in efficacy between antidepressant therapies are small.


To determine the efficacy of antidepressant treatments on empirically defined groups of symptoms and examine the replicability of these groups.

Design, Setting, and Participants:

Patient-reported data on patients with depression from the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) trial (n = 4039) were used to identify clusters of symptoms in a depressive symptom checklist. The findings were then replicated using the Combining Medications to Enhance Depression Outcomes (CO-MED) trial (n = 640). Mixed-effects regression analysis was then performed to determine whether observed symptom clusters have differential response trajectories using intent-to-treat data from both trials (n = 4706) along with 7 additional placebo and active-comparator phase 3 trials of duloxetine (n = 2515). Finally, outcomes for each cluster were estimated separately using machine-learning approaches. The study was conducted from October 28, 2014, to May 19, 2016.

Main Outcomes and Measures:

Twelve items from the self-reported Quick Inventory of Depressive Symptomatology (QIDS-SR) scale and 14 items from the clinician-rated Hamilton Depression (HAM-D) rating scale. Higher scores on the measures indicate greater severity of the symptoms.


Of the 4706 patients included in the first analysis, 1722 (36.6%) were male; mean (SD) age was 41.2 (13.3) years. Of the 2515 patients included in the second analysis, 855 (34.0%) were male; mean age was 42.65 (12.17) years. Three symptom clusters in the QIDS-SR scale were identified at baseline in STAR*D. This 3-cluster solution was replicated in CO-MED and was similar for the HAM-D scale. Antidepressants in general (8 of 9 treatments) were more effective for core emotional symptoms than for sleep or atypical symptoms. Differences in efficacy between drugs were often greater than the difference in efficacy between treatments and placebo. For example, high-dose duloxetine outperformed escitalopram in treating core emotional symptoms (effect size, 2.3 HAM-D points during 8 weeks, 95% CI, 1.6 to 3.1; P < .001), but escitalopram was not significantly different from placebo (effect size, 0.03 HAM-D points; 95% CI, -0.7 to 0.8; P = .94).

Conclusions and Relevance:

Two common checklists used to measure depressive severity can produce statistically reliable clusters of symptoms. These clusters differ in their responsiveness to treatment both within and across different antidepressant medications. Selecting the best drug for a given cluster may have a bigger benefit than that gained by use of an active compound vs a placebo.

[Indexed for MEDLINE]
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