Abstract
Background: One in three clinical trial patients with major depressive disorder report symptomatic improvement with placebo. Strategies to mitigate the effect of placebo responses have focused on modifying study design with variable success. Identifying and excluding or controlling for individuals with a high likelihood of responding to placebo may improve clinical trial efficiency and avoid unnecessary medication trials. Methods: Participants included those assigned to the placebo arm (n = 141) of the Establishing Moderators and Biosignatures for Antidepressant Response in Clinical Care (EMBARC) trial. The elastic net was used to evaluate 283 baseline clinical, behavioral, imaging, and electrophysiological variables to identify the most robust yet parsimonious features that predicted depression severity at the end of the double-blind 8-week trial. Variables retained in at least 50% of the 100 imputed data sets were used in a Bayesian multiple linear regression model to simultaneously predict the probabilities of response and remission. Results: Lower baseline depression severity, younger age, absence of melancholic features or history of physical abuse, less anxious arousal, less anhedonia, less neuroticism, and higher average theta current density in the rostral anterior cingulate predicted a higher likelihood of improvement with placebo. The Bayesian model predicted remission and response with an actionable degree of accuracy (both AUC > 0.73). An interactive calculator was developed predicting the likelihood of placebo response at the individual level. Conclusion: Easy-to-measure clinical, behavioral, and electrophysiological assessments can be used to identify placebo responders with a high degree of accuracy. Development of this calculator based on these findings can be used to identify potential placebo responders.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 285-295 |
| Number of pages | 11 |
| Journal | Psychotherapy and Psychosomatics |
| Volume | 87 |
| Issue number | 5 |
| DOIs | |
| State | Published - Sep 1 2018 |
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Keywords
- EMBARC trial
- Placebo responder
- Prediction index
ASJC Scopus subject areas
- Clinical Psychology
- Applied Psychology
- Psychiatry and Mental health
Cite this
A Novel Strategy to Identify Placebo Responders : Prediction Index of Clinical and Biological Markers in the EMBARC Trial. / Trivedi, Madhukar H.; South, Charles; Jha, Manish K.; Rush, A. John; Cao, Jing; Kurian, Benji; Phillips, Mary; Pizzagalli, Diego A.; Trombello, Joseph M.; Oquendo, Maria A.; Cooper, Crystal; Dillon, Daniel G.; Webb, Christian; Grannemann, Bruce D.; Bruder, Gerard; McGrath, Patrick J.; Parsey, Ramin; Weissman, Myrna; Fava, Maurizio.
In: Psychotherapy and Psychosomatics, Vol. 87, No. 5, 01.09.2018, p. 285-295.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - A Novel Strategy to Identify Placebo Responders
T2 - Prediction Index of Clinical and Biological Markers in the EMBARC Trial
AU - Trivedi, Madhukar H.
AU - South, Charles
AU - Jha, Manish K.
AU - Rush, A. John
AU - Cao, Jing
AU - Kurian, Benji
AU - Phillips, Mary
AU - Pizzagalli, Diego A.
AU - Trombello, Joseph M.
AU - Oquendo, Maria A.
AU - Cooper, Crystal
AU - Dillon, Daniel G.
AU - Webb, Christian
AU - Grannemann, Bruce D.
AU - Bruder, Gerard
AU - McGrath, Patrick J.
AU - Parsey, Ramin
AU - Weissman, Myrna
AU - Fava, Maurizio
PY - 2018/9/1
Y1 - 2018/9/1
N2 - Background: One in three clinical trial patients with major depressive disorder report symptomatic improvement with placebo. Strategies to mitigate the effect of placebo responses have focused on modifying study design with variable success. Identifying and excluding or controlling for individuals with a high likelihood of responding to placebo may improve clinical trial efficiency and avoid unnecessary medication trials. Methods: Participants included those assigned to the placebo arm (n = 141) of the Establishing Moderators and Biosignatures for Antidepressant Response in Clinical Care (EMBARC) trial. The elastic net was used to evaluate 283 baseline clinical, behavioral, imaging, and electrophysiological variables to identify the most robust yet parsimonious features that predicted depression severity at the end of the double-blind 8-week trial. Variables retained in at least 50% of the 100 imputed data sets were used in a Bayesian multiple linear regression model to simultaneously predict the probabilities of response and remission. Results: Lower baseline depression severity, younger age, absence of melancholic features or history of physical abuse, less anxious arousal, less anhedonia, less neuroticism, and higher average theta current density in the rostral anterior cingulate predicted a higher likelihood of improvement with placebo. The Bayesian model predicted remission and response with an actionable degree of accuracy (both AUC > 0.73). An interactive calculator was developed predicting the likelihood of placebo response at the individual level. Conclusion: Easy-to-measure clinical, behavioral, and electrophysiological assessments can be used to identify placebo responders with a high degree of accuracy. Development of this calculator based on these findings can be used to identify potential placebo responders.
AB - Background: One in three clinical trial patients with major depressive disorder report symptomatic improvement with placebo. Strategies to mitigate the effect of placebo responses have focused on modifying study design with variable success. Identifying and excluding or controlling for individuals with a high likelihood of responding to placebo may improve clinical trial efficiency and avoid unnecessary medication trials. Methods: Participants included those assigned to the placebo arm (n = 141) of the Establishing Moderators and Biosignatures for Antidepressant Response in Clinical Care (EMBARC) trial. The elastic net was used to evaluate 283 baseline clinical, behavioral, imaging, and electrophysiological variables to identify the most robust yet parsimonious features that predicted depression severity at the end of the double-blind 8-week trial. Variables retained in at least 50% of the 100 imputed data sets were used in a Bayesian multiple linear regression model to simultaneously predict the probabilities of response and remission. Results: Lower baseline depression severity, younger age, absence of melancholic features or history of physical abuse, less anxious arousal, less anhedonia, less neuroticism, and higher average theta current density in the rostral anterior cingulate predicted a higher likelihood of improvement with placebo. The Bayesian model predicted remission and response with an actionable degree of accuracy (both AUC > 0.73). An interactive calculator was developed predicting the likelihood of placebo response at the individual level. Conclusion: Easy-to-measure clinical, behavioral, and electrophysiological assessments can be used to identify placebo responders with a high degree of accuracy. Development of this calculator based on these findings can be used to identify potential placebo responders.
KW - EMBARC trial
KW - Placebo responder
KW - Prediction index
UR - http://www.scopus.com/inward/record.url?scp=85052687658&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85052687658&partnerID=8YFLogxK
U2 - 10.1159/000491093
DO - 10.1159/000491093
M3 - Article
C2 - 30110685
AN - SCOPUS:85052687658
VL - 87
SP - 285
EP - 295
JO - Psychotherapy and Psychosomatics
JF - Psychotherapy and Psychosomatics
SN - 0033-3190
IS - 5
ER -