Implications of a Biosignature Study of the Placebo Response in Major Depressive Disorder

Fava, Maurizio

doi:10.1001/jamapsychiatry.2015.1727

Cited by 13 publications

(8 citation statements)

References 7 publications

(8 reference statements)

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“…However, both placebo and nocebo effects as well as responses are key themes for further study in health research. For example, the probability of placebo assignments frequently contributes to the placebo response in clinical trials, showing larger effects in trials with a higher chance of getting the real treatment in comparison to the placebo conditions [2, 3, 41, 42]. The addition of a hidden or unpredictable prescription of the treatment might be a future path to minimize these placebo responses in clinical trials [2, 3].…”

Section: Discussionmentioning

confidence: 99%

Implications of Placebo and Nocebo Effects for Clinical Practice: Expert Consensus

Evers

Colloca

Blease

et al. 2018

Psychother Psychosom

368

420

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Background: Placebo and nocebo effects occur in clinical or laboratory medical contexts after administration of an inert treatment or as part of active treatments and are due to psychobiological mechanisms such as expectancies of the patient. Placebo and nocebo studies have evolved from predominantly methodological research into a far-reaching interdisciplinary field that is unravelling the neurobiological, behavioural and clinical underpinnings of these phenomena in a broad variety of medical conditions. As a consequence, there is an increasing demand from health professionals to develop expert recommendations about evidence-based and ethical use of placebo and nocebo effects for clinical practice. Methods: A survey and interdisciplinary expert meeting by invitation was organized as part of the 1st Society for Interdisciplinary Placebo Studies (SIPS) conference in 2017. Twenty-nine internationally recognized placebo researchers participated. Results: There was consensus that maximizing placebo effects and minimizing nocebo effects should lead to better treatment outcomes with fewer side effects. Experts particularly agreed on the importance of informing patients about placebo and nocebo effects and training health professionals in patient-clinician communication to maximize placebo and minimize nocebo effects. Conclusions: The current paper forms a first step towards developing evidence-based and ethical recommendations about the implications of placebo and nocebo research for medical practice, based on the current state of evidence and the consensus of experts. Future research might focus on how to implement these recommendations, including how to optimize conditions for educating patients about placebo and nocebo effects and providing training for the implementation in clinical practice.

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Section: Discussionmentioning

confidence: 99%

Implications of Placebo and Nocebo Effects for Clinical Practice: Expert Consensus

Evers

Colloca

Blease

et al. 2018

Psychother Psychosom

368

420

View full text Add to dashboard Cite

show abstract

“…However, other factors such as gender and age, while significant in venlafaxine versus placebo studies [18], did not replicate in a meta-analysis by Holmes et al [19]. The neurobiological basis of the placebo response is characterized by an increase in the metabolic activity of the frontal and striatal cortical regions [20] and increased endogenous opioid release in the subgenual anterior cingulate cortex, nucleus accumbens, midline thalamus, and amygdala [21,22]. The placebo response has also been linked to increased baseline resting state functional connectivity of the rostral anterior cingulate cortex (rACC) within the salience network [23] and to increased pretreatment rACC activity in 2 EEG studies [24,25].…”

Section: Introductionmentioning

confidence: 99%

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

Trivedi

South

Rush³

et al. 2018

Psychother Psychosom

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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.

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“…While the speci c effect can be associated with the active drug response, the non-speci c effect can be attributed to a generic individual propensity to respond to any treatment or intervention. As we have previously described [31], one may classify treated patients in an MDD trial based on each participant's propensity to respond to a given type of treatment. The "D − P−" population comprises patients who are not responsive to either active treatment (D) or placebo treatment (P), whereas the "D + P−" population comprises patients who are responsive to active treatment but not to placebo.…”

Section: Discussionmentioning

confidence: 99%

Artificial intelligence approach for the analysis of placebo-controlled clinical trials in major depressive disorders accounting for individual propensity to respond to placebo

Goméni¹,

Bressolle-Gomeni²,

Fava

2023

Preprint

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Treatment effect in clinical trials for major depressive disorders (RCT) can be viewed as the resultant of treatment specific and non-specific effects. Baseline individual propensity to respond non-specifically to any treatment or intervention can be considered as a major non-specific confounding effect. The greater is the baseline propensity, the lower will be the chance to detect any treatment-specific effect. The statistical methodologies currently applied for analyzing RCTs doesn’t account for potential unbalance in the allocation of subjects to treatment arms due to heterogenous distributions of propensity. Hence, the groups to be compared may be imbalanced, and thus incomparable. Propensity weighting methodology was used to reduce baseline imbalances between arms. A randomized, double-blind, placebo controlled, three arms, parallel group, 8-week, fixed-dose study to evaluate efficacy of paroxetine CR 12.5 and 25 mg/day is presented as a cases study. An artificial intelligence model was developed to predict placebo response at week 8 in subjects assigned to placebo arm using changes from screening to baseline of individual Hamilton Depression Rating Scale items. This model was used to predict the probability to respond to placebo in each subject. The inverse of the probability was used as weight in the mixed-effects model applied to assess treatment effect. The analysis with and without propensity weight indicated that the weighted analysis provided an estimate of treatment effect and effect-size about twice larger than the non-weighted analysis. Propensity weighting provides an unbiased strategy to account for heterogeneous and uncontrolled placebo effect making patients’ data comparable across treatment arms.

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Implications of a Biosignature Study of the Placebo Response in Major Depressive Disorder

Cited by 13 publications

References 7 publications

Implications of Placebo and Nocebo Effects for Clinical Practice: Expert Consensus

Implications of Placebo and Nocebo Effects for Clinical Practice: Expert Consensus

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

Artificial intelligence approach for the analysis of placebo-controlled clinical trials in major depressive disorders accounting for individual propensity to respond to placebo

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