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

doi:10.1001/JAMAPSYCHIATRY.2017.0025

Reevaluating the Efficacy and Predic tability

of Antidepressant Treatments

A Symptom Clustering Approach

Adam M. Chekroud, MSc; Ralitza Gueorguieva, PhD; Harlan M. Krumholz, MD, SM; Madhukar H. Trivedi, MD;

John H. Krystal, MD; Gregory McCarthy, PhD

IMPORTANCE

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.

OBJECTIVES To determine the efficacy of antidepressant treatments on empirically defined

groups of symptoms and examine the replicability of the se 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.

RESULTS 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. Antidepre ssants 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.

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

Published online February 22, 2017.

Supplemental content

Author Affiliations: Author

affiliations are listed at the end of this

article.

Corresponding Author: Adam M.

Chekroud, MSc, Department of

Psychology, Yale University, 2

Hillhouse Ave, New Haven, CT 06511

(adam.chekroud@yale.edu).

Research

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M

eta-analyses

1

and factor analytic studies of large popu-

lations with depression

2,3

indicate that the symp-

toms of major depressive disorder are organized into

2 to 5 clusters depending on the checklist used. Nevertheless,

clinical trials of patients with depression nearly always report

total symptom severity scores as their primary outcome mea-

sures. These studies also frequently report the proportion of pa-

tients whose total symptom severityfalls below a certain thresh-

old and thus achieve clinical response or remission.

4

Few

patients reach remission with their initial treatment, although

depression eventually remits in most patientsafter a largely trial-

and-error treatment selection process.

5

Statistical models might

improve clinical outcomes by accelerating the treatment match-

ing process. Despite concerted efforts using genomic data,

6

structural and functional magnetic resonance imaging,

7

and ma-

chine learning of clinical data,

8

performance in predicting out-

comes remains modest.

9,10

Heterogeneity among depressive symptoms may im-

pede the evaluation of treatments for depression.

11,12

For ex-

ample, treatment efficacy for one group of symptoms may be

masked by a lack of efficacy for other symptoms, potentially

explaining mixed results from large comparative efficacy

meta-analyses.

4,13

For example, selective serotonin reuptake

inhibitors are generally effective in reducing low mood

14

rela-

tive to other symptoms. However, evaluating outcomes on an

individual symptom level may be cumbersome since clini-

cians would need to remember treatment guidelines specific

to each symptom. Although symptoms might be grouped based

on clinical experience (eg, “melancholic depression”)

15

or

the use of rating subscales (eg, Hamilton Rating Scale for

Depression–7), novel associations might be overlooked by this

process.

Statistical methods enable one to categorize depressive

symptoms into subcomponents. For example, one study

showed that nortriptyline hydrochloride is more effective than

escitalopram in treating a neurovegetative symptom dimen-

sion, but escitalopram was more effective in treating mood and

cognitive symptom dimensions.

16

However, traditional sta-

tistical approaches have some shortcomings. Factor analy-

ses, for example, may generate complicated combinations of

symptoms within particular dimensions.

16

These analyses also

may be susceptible to experimenter bias since one often has

to choose the desired number of clusters or components in the

data, as in k means clustering.

17

By contrast, hierarchical clus-

tering is an easy-to-visualize, deterministic method in which

each symptom is assigned to a single cluster (ie, not loading

across multiple clusters) without prespecifying the desired the

number of clusters.

In this study, we explored the efficacy and predictability

of antidepressant therapies in treating specific groups of symp-

toms (eMethods [which includes eTables 1-10 of various analy-

ses] and eFigure 1 in the Supplement). We used an unsuper-

vised machine-learning approach (hierarchical clustering) to

establish a data-driven grouping of baseline symptoms. The

clustering method was applied to patients from a large mul-

tisite trial of depression and a replication sample from an in-

dependent clinical trial with similar inclusion criteria. Next,

we reanalyzed treatment outcomes for 9 archival clinical trials

(Table 1) according to the severity of each symptom cluster

(rather than total severity) to determine whether symptom

clusters are equally responsive to antidepressant treatments

and whether certain drugs and doses are more effective than

others. Finally, we used supervised machine learning to pre-

dict outcomes specific to each cluster of symptoms since there

may be good clinical or biological indicators of changes in some

symptoms that do not correlate strongly with changes in other

features of depression.

Methods

Clinical Trial Data

The Sequenced Treatment Alternatives to Relieve Depres-

sion (STAR*D) trial is the largest prospective, randomized clini-

cal trial of outpatients with major depressive disorder.

18-21

Eli-

gible participants were treatment-seeking outpatients with a

primary clinical (DSM-IV) diagnosis of nonpsychotic major de-

pressive disorder scored 14 or higher on the 17-item Hamilton

Depression (HAM-D) rating scale, were aged 18 to 75 years, and

were recruited from primary and psychiatric care settings in

the United States from June 2001 to April 2004.

19

We focused

on the first treatment stage consisting of a 12-week course of

citalopram hydrobromide. The present study was conducted

from October 28, 2014, to May 19, 2016. It was approved by

the Yale University Human Subjects Committee, with a waiver

of informed consent.

The Combining Medications to Enhance Depression

Outcomes (CO-MED) trial was a multisite, single-blind, ran-

domized clinical trial comparing the efficacy of medication

combinations in the treatment of unipolar major depressive

disorder.

22,23

Eligible patients were aged 18 to 75 years, had a

primary DSM-IV–based diagnosis of nonpsychotic major

depressive disorder, had recurrent or chronic depression

(current episode ≥2 years), scored 16 or higher on the 17-item

HAM-D rating scale, and enrolled participants between

March 2008 and February 2009. Patients were randomly

allocated (1:1:1) to escitalopram plus placebo (monotherapy),

escitalopram plus bupropion hydrochloride, or venlafaxine

hydrochloride plus mirtazapine.

We also analyzed all arms from 7 randomized, multicenter,

double-blind, placebo-controlled, and active comparator-

controlled clinical trials of duloxetine for major depressive

disorder (Table 1). Four different protocols were used for these

Key Points

Question Are antidepressants equally good at treating different

kinds of symptoms in depression?

Findings Individual patient data from 9 clinical trials of major

depression in 7221 patients were analyzed, with a focus on specific

clusters of symptoms rather than total depressive severity. For

each cluster, significant differences in efficacy between

antidepressants were identified.

Meaning Antidepressant medications can be selected to benefit

specific clusters of symptoms in depre ssion.

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studies; parts A and B reflect trials run in parallel following

the same protocol. All studies incorporated double-

blind, variable-duration placebo lead-in periods. Safety and

efficacy results from these studies have been published

previously

24-27

and summarized as pooled analyses of

safety

28

and efficacy.

29

Study HMCR is registered at

clinicaltrials.gov.

30

The other studies were conducted before

clinical trial registration was necessary.

Outcomes for STAR*D and CO-MED are based on the

16-item self-report Quick Inventory of Depressive Symp-

tomatology (QIDS-SR) checklist during 12 weeks of treat-

ment. Outcomes for all other trials are based on the 17-item

HAM-D rating scale

31

during 8 weeks. We excluded the

HAM-D “loss of insight” item because there is no equivalent

in the QIDS-SR and excluded weight/appetite items because

they were not collected in the same way across trials and are

often excluded from item-level analyses

32

(eFigure 2 in the

Supplement). Study selection was driven primarily by

access to individual patient-level data. Patients provided

informed consent to treatment when they participated in

the original clinical trials. Consent was not needed for the

present analyses since the data were deidentified. 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.

Symptom Clustering

Rating scales in depression include a diverse range of symp-

toms. We applied a data-driven approach to identify groups

of symptoms within depression rating scales. Higher scores on

the rating scales indicate more severe symptoms. Hierarchi-

cal clustering shows structure in data without making assump-

tions about the number of clusters that are present in the data

and gives a deterministic solution. We applied agglomerative

(bottom-up) hierarchical clustering to the QIDS-SR checklist

completed at baseline in STAR*D by 4017 patients and repli-

cated the analysis using baseline QIDS-SR data from CO-MED

(n = 640) and the baseline HAM-D scale that was also col-

lected on 4039 patients in STAR*D. We conducted multiple sen-

sitivity analyses using alternative approaches (eFigures 3-9 and

eTables 1-3 in the Supplement).

Table 1. Individual Patient-Level Data Aggregated From 9 Trials of Antidepressant Efficacy for Unipolar

Major Depression

Protocol

Sample Size

(N = 7221) Treatment Dose

STAR*D phase 1 4041 Citalopram 20-60 mg once daily

CO-MED 224 Escitalopram plus placebo 10-20 mg once daily

221 Escitalopram plus bupropion

extended release

Escitalopram, 10-20 mg once daily; bupropion

extended release, 150-200 mg twice daily

220 Venlafaxine extended release

plus mirtazapine

Venlafaxine extended release, 37.5-300 mg once

daily; mirtazapine, 15-45 mg once daily

HMAQ part A 70 Duloxetine 20-60 mg twice daily

33 Fluoxetine 20 mg once daily

70 Placebo NA

HMAQ part B 82 Duloxetine 20-60 mg twice daily

37 Fluoxetine 20 mg once daily

74 Placebo NA

HMAT part A 91 Duloxetine 20 mg twice daily

84 Duloxetine 40 mg twice daily

89 Paroxetine 20 mg once daily

90 Placebo NA

HMAT part B 86 Duloxetine 20 mg twice daily

91 Duloxetine 40 mg twice daily

87 Paroxetine 20 mg once daily

89 Placebo NA

HMAY part A 95 Duloxetine 40 mg twice daily

93 Duloxetine 60 mg twice daily

86 Paroxetine 20 mg once daily

93 Placebo NA

HMAY part B 93 Duloxetine 40 mg twice daily

103 Duloxetine 60 mg twice daily

97 Paroxetine 20 mg once daily

99 Placebo NA

HMCR 273 Duloxetine 60 mg twice daily

273 Escitalopram 10 mg once daily

137 Placebo NA

Abbreviations: CO-MED, Combining

Medications to Enhance Depression

Outcomes; NA, not applicable;

STAR*D, Sequenced Treatment

Alternatives to Relieve Depression.

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Evaluation of Treatment Outcomes

Treatment Efficacy

We analyzed the full intent-to-treat samples in all trials using

linear mixed-effects regression models (STAR*D, 4041; CO-

MED, 665; and other trials, 2515). The dependent measure was

mean within-cluster severity: for each patient at each time

point, we calculated the mean symptom severity within each

cluster. Fixed effects included symptom cluster, time (log-

transformed weeks), treatment regimen, and all 2- and 3-way

interaction effects. We included a separate random intercept

and slope for each symptom cluster with unstructured vari-

ance-covariance of the random effects within subject based on

improvements in the Schwarz-Bayesianinformation criterion.

33

False-discovery rate-adjusted

34

P values were used to deter-

mine statistical significance for post hoc comparisons by clus-

ter and drug within each mixed-model analysis.

One model was used to analyze QIDS-SR–based clusters

across STAR*D and CO-MED, and another model was used to

analyze HAM-D–based clusters for the 7 other placebo-

controlled trials. In the HAM-D model, we also included the

main effect of the trial to control for potential systematic dif-

ferences among trials. Preliminary analyses of the 4 dulox-

etine doses in each cluster indicated that 120-mg/d and 80-

mg/d dosages were not significantly different from each other

but differed from the lower doses and placebo (eResults and

eFigure 10 in the Supplement). The 60-mg/d and 40-mg/d du-

loxetine dosages were similar to each other and nearly indis-

tinguishable from placebo. We therefore grouped cohorts into

high-dose duloxetine (80-120 mg/d) and low-dose dulox-

etine (40-60 mg/d).

Outcome Predictability

We used a recently developed statistical modeling pipeline

8

to

predict treatment outcomes specific to each symptom cluster

using information available at baseline. We extracted 164 items,

including demographics, medical and psychiatric histories, and

specific symptom items that were used as predictor variables

(eTable 10 in the Supplement). Penalized logistic regression (elas-

tic net

35,36

) was then used to identify the 25 variables that best

predicted each cluster separately. These variables were then

used to train machine-learning algorithms (gradient boosting

machines

37,38

), resulting in a separate model for each symp-

tom cluster, with each using 25 predictor variables. Predictabil-

ity was measured as the percentage of variance explained in

final cluster scores (ie, R

2

) using 5 repeats of 10-fold cross-

validation. The statistical significance of each model was as-

sessed using a permutation test (eMethods in the Supple-

ment). We trained models on patients with complete baseline

data for whom a severity score was recorded after 12 or more

weeks of treatment (n = 1962) to ensure adequate treatment du-

ration. To externally validate our predictive models, they were

applied without modification to predict final cluster scores in

CO-MED treatment completers. Here, statistical significance was

measured by a P value calculated for Pearson correlations be-

tween predicted outcomes and observed outcomes in each treat-

ment group of CO-MED. We did not have comparable predic-

tor data in the duloxetine trials; thus, predictive analyses were

conducted only for STAR*D and CO-MED. For significance, per-

mutation-based tests used an α level of .01, mixed-effects re-

gressions used a false-discovery rate correction and then an α

level of .05, and Pearson correlations used an α level of .05.

Predictive and clustering analyses were implemented in

R, version 3.2.3 (R Foundation). Efficacy analyses were con-

ducted using SAS, version 9.4 (proc mixed) (SAS Institute).

Results

In 2 independent trials, we identified the same clustering of

symptoms in the QIDS-SR checklist, consisting of core emo-

tional, sleep (insomnia), and atypical symptoms (Figure 1A and

B). A similar clustering solution was also found for the HAM-D

scale checklist (Figure 1C). The clustering solution was robust

across a number of sensitivity analyses using different para-

meters, time points, and approaches (eFigures 3-9 and eTables

1-3intheSupplement).

Efficacy Analyses

Treatment efficacy was measured according to the rate of

symptom improvement over time (ie, steeper symptom tra-

jectories are better, as shown in Figure 2). No antidepressant

treatment worked equally well across all 3 symptom clusters.

As shown in Figure 2A, when measured according to the QIDS-

SR, trajectories were significantly better for core emotional

symptoms than for either sleep symptoms or atypical symp-

toms for citalopram, escitalopram with placebo, and escita-

lopram with bupropion (all β>0.079; all false-discovery rate

corrected P < .001). Sleep trajectories were also better than

atypical trajectories for these 3 treatments (all β>0.099; all

P ≤ .001). As shown in Figure 2B, when measured according

to the HAM-D rating scale, a similar pattern was observed. Core

emotional trajectories were better than sleep and atypical

trajectories for all treatments (all β>0.12; all P ≤ .001). Sleep

trajectories were also better than atypical trajectories for low-

dose duloxetine and escitalopram (all β>0.080; all P ≤ .001).

All slope contrast estimates, SEs, 95% CIs, and P values are

included in eTables 4 and 5 in the Supplement.

To interpret the magnitude of differences between drugs,

we calculated an effect size (ES), measured in raw rating scale

points, that reflects the difference between treatments in re-

ducing the overall severity of a symptom cluster (ie, we mul-

tiplied slope contrasts by the natural log of treatment dura-

tion and then by the number of symptoms in each cluster). For

example, in this study, high-dose duloxetine was signifi-

cantly better than escitalopram in treating atypical symp-

toms, such that a patient’s total improvement in atypical

severity was a mean of 1.9 HAM-D points greater with high-

dose duloxetine than escitalopram (ES, 1.9; 95% CI, 1.4-2.3;

false-discovery rate corrected P < .001).

For each symptom cluster, there were significant differ-

ences in efficacy between treatments (Figure 2). Combined

escitalopram and bupropion treatment was significantly more

effective in treating core emotional symptoms than citalo-

pram (ES, 0.7 QIDS-SR points; 95% CI, 0.2 to 1.3; P = .03). For

sleep/insomnia symptoms, venlafaxine with mirtazapine out-

performed citalopram (ES, 1.4; 95% CI, 1.0 to 1.8; P < .001). For

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core emotional symptoms in HAM-D scale trials (Figure 2B),

high-dose duloxetine outperformed escitalopram (ES, 2.3

HAM-D points; 95% CI, 1.6 to 3.1; P < .001). Escitalopram was

not significantly different from placebo for core emotional

symptoms (ES, 0.03 HAM-D points; 95% CI, −0.7 to 0.8;

P = .94). For sleep symptoms, high-dose duloxetine outper-

formed fluoxetine (ES, 0.9; 95% CI, 0.1 to 1.7; P = .046). For

atypical symptoms, high-dose duloxetine outperformed all oth-

ers (ES, 0.5-1.9) and escitalopram was worse than placebo (ES,

0.7; 95% CI, 0.3 to 1.1; P = .002). Among our HAM-D studies,

only 2 antidepressant treatments (high-dose duloxetine and

paroxetine) outperformed placebo for all 3 symptom clus-

ters. All other comparisons are presented in eTables 6 and 7

in the Supplement.

Predictive Analyses

Within STAR*D, although all models performed significantly

above chance (all P < .01), we observed substantial variability

in the predictability of outcomes for each cluster (Table 2 and

eTable 8 in the Supplement). The sleep symptom cluster was

the most predictable (R

2

= 19.6%; SD, 5.0%; P < .01) and sub-

stantially more predictable than core symptoms (R

2

= 14.5%;

SD, 4.6%; P < .01) and atypical symptoms (R

2

= 15.1%; SD, 5.3%;

P < .01). The observed range in cluster predictability (R

2

difference, 5.1%) was also significantly larger than any range

observed during permutation testing (mean [SD] range, 0.56%

[0.50%]; P < .01). We inspected the best predictive baseline

variables for each model separately, highlighting those iden-

tified as predictive for 1 cluster but not others (ie, specific pre-

dictors) (Table 2). Baseline HAM-D scale severity was a top pre-

dictor of core emotional outcomes but not any of the other 3

clusters. Baseline atypical symptom severity and hypersom-

nia predicted atypical outcomes; baseline sleep cluster sever-

ity and early-morning insomnia predicted sleep outcomes.

We then applied the best-performing models, without

modification, to predict outcomes for each cluster in the 3 treat-

ment groups of CO-MED (Figure 3). Performance was statisti-

cally above chance, although clinically modest, for predict-

ing core emotional outcomes in the escitalopram monotherapy

arm (r

149

= 0.18; P = .03) and the venlafaxine-mirtazapine arm

(r

138

=0.17;P = .04). Performance was above chance predict-

ing sleep outcomes in the escitalopram-bupropion arm

(r

132

= 0.36; P < .001).

Clinical Decision Support Tool

To help translate these findings into clinical practice, we based

a clinical decision support tool on these findings. It is imple-

mented as a brief questionnaire that can be accessed from any

web browser and returns results in real time (https://www

.spring.care/spring-assessment).

Discussion

Using a data-driven approach, we identified 3 symptom clus-

ters within the QIDS-SR checklist. We replicated our cluster-

ing solution in an independent trial cohort (CO-MED) and found

it to be robust across different parameters and time points and

consistent with other statistical approaches. No antidepres-

sant was equally effective for all 3 symptom clusters, and, for

each symptom cluster, there were significant differences in

treatment efficacy between drugs. Antidepressants in gen-

eral worked best in treating core emotional and sleep symp-

toms and were less effective in treating atypical symptoms. The

magnitude of these differences suggests that selecting the best

drug for a given cluster may have a bigger benefit than that

Figure 1. Data-Driven Decomposition of Depressive Checklists

Using Hierarchical Clustering

Midnocturnal insomnia

Sleep-onset insomnia

Early morning insomnia

Energy/fatigability

Concentration/decision making

Loss of interest

Mood (sad)

Feelings of worthlessness

Psychomotor agitation

Psychomotor slowing

Suicidal ideation

Hypersomnia

Midnocturnal insomnia

Sleep-onset insomnia

Early morning insomnia

Energy/fatigability

Concentration/decision making

Loss of interest

Mood (sad)

Feelings of worthlessness

Psychomotor agitation

Psychomotor slowing

Suicidal ideation

Hypersomnia

Reduced libido

Psychomotor slowing

Suicide

Psychomotor agitation

Hypochondriasis

Energy/fatigability

Midnocturnal insomnia

Slee

p-o

nset insomnia

Early morning insomnia

Somatic anxiety

Psychological anxiety

Guilt and delusions

Loss of interest

Mood (sad)

Sleep (insomnia)

Core emotional

Atypical

Total QIDS-SR

Severity

Total QIDS-SR

Severity

Total HAM-D

Severity

QIDS-SR in STAR*D

A

QIDS-SR in CO-MED

B

HAM-D in STAR*D

C

This procedure sequentially groups symptoms according to the similarity of

their responses across a patient cohort. With this procedure, groups of

symptoms that merge at high values relative to the merge points of their

subgroups are considered candidates for natural clusters. A and B, In the Quick

Inventory of Depressive Symptomatology–Self Report (QIDS-SR) checklist, we

identified an identical 3-cluster solution in both the Sequenced Treatment

Alternatives to Relieve Depression (STAR*D) (n = 4017) and Combining

Medications to Enhance Depression Outcomes (CO-MED) trials (n = 640).

C, A comparable symptom structure was also observed at baseline for STAR*D

patients when measured according to the Hamilton Depression (HAM-D) rating

scale. The name s of the individual checklist items are colored according to their

cluster assignment. Line leng ths in the dendogram reflect how similar items or

clusters are to one another (shorter line length indicates greater similarity).

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

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