A Novel Strategy to Identify Placebo Responders: Prediction Index of Clinical and Biological Markers in the EMBARC Trial

Madhukar H. Trivedi, Charles South, Manish K. Jha, A. John Rush, Jing Cao, Benji Kurian, Mary Phillips, Diego A. Pizzagalli, Joseph M. Trombello, Maria A. Oquendo, Crystal Cooper, Daniel G. Dillon, Christian Webb, Bruce D. Grannemann, Gerard Bruder, Patrick J. McGrath, Ramin Parsey, Myrna Weissman, Maurizio Fava

Research output: Contribution to journalArticle

5 Citations (Scopus)

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 languageEnglish (US)
Pages (from-to)285-295
Number of pages11
JournalPsychotherapy and Psychosomatics
Volume87
Issue number5
DOIs
StatePublished - Sep 1 2018

Fingerprint

Antidepressive Agents
Biomarkers
Placebos
Clinical Trials
Linear Models
Depression
Anhedonia
Placebo Effect
Gyrus Cinguli
Major Depressive Disorder
Arousal
Area Under Curve

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 journalArticle

Trivedi, MH, South, C, Jha, MK, Rush, AJ, Cao, J, Kurian, B, Phillips, M, Pizzagalli, DA, Trombello, JM, Oquendo, MA, Cooper, C, Dillon, DG, Webb, C, Grannemann, BD, Bruder, G, McGrath, PJ, Parsey, R, Weissman, M & Fava, M 2018, 'A Novel Strategy to Identify Placebo Responders: Prediction Index of Clinical and Biological Markers in the EMBARC Trial', Psychotherapy and Psychosomatics, vol. 87, no. 5, pp. 285-295. https://doi.org/10.1159/000491093
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. / A Novel Strategy to Identify Placebo Responders : Prediction Index of Clinical and Biological Markers in the EMBARC Trial. In: Psychotherapy and Psychosomatics. 2018 ; Vol. 87, No. 5. pp. 285-295.
@article{cb87200569c44ab29b756723605657fe,
title = "A Novel Strategy to Identify Placebo Responders: Prediction Index of Clinical and Biological Markers in the EMBARC Trial",
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.",
keywords = "EMBARC trial, Placebo responder, Prediction index",
author = "Trivedi, {Madhukar H.} and Charles South and Jha, {Manish K.} and Rush, {A. John} and Jing Cao and Benji Kurian and Mary Phillips and Pizzagalli, {Diego A.} and Trombello, {Joseph M.} and Oquendo, {Maria A.} and Crystal Cooper and Dillon, {Daniel G.} and Christian Webb and Grannemann, {Bruce D.} and Gerard Bruder and McGrath, {Patrick J.} and Ramin Parsey and Myrna Weissman and Maurizio Fava",
year = "2018",
month = "9",
day = "1",
doi = "10.1159/000491093",
language = "English (US)",
volume = "87",
pages = "285--295",
journal = "Psychotherapy and Psychosomatics",
issn = "0033-3190",
publisher = "S. Karger AG",
number = "5",

}

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 -