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Figure 1.  Preferred Reporting Items for Systematic Reviews and Meta-analyses Flow Diagram for Inclusion of Studies
Preferred Reporting Items for Systematic Reviews and Meta-analyses Flow Diagram for Inclusion of Studies
Figure 2.  Main Placebo Effect Size of Included Pill and tDCS Studies
Main Placebo Effect Size of Included Pill and tDCS Studies

Studies were grouped by treatment modality and pooled. tDCS indicates transcranial direct current stimulation.

Figure 3.  Main Placebo Effect Size of Included rTMS, Invasive Brain Stimulation, Parenteral, Ayahuasca, and Overall Studies
Main Placebo Effect Size of Included rTMS, Invasive Brain Stimulation, Parenteral, Ayahuasca, and Overall Studies

Studies were grouped by treatment modality and pooled. Ayahuasca is a South American psychoactive brew. rTMS indicates repetitive transcranial magnetic stimulation.

Table 1.  Demographic, Clinical, and Methodological Characteristics of Included Studies in the Systematic Review and Meta-analysis
Demographic, Clinical, and Methodological Characteristics of Included Studies in the Systematic Review and Meta-analysis
Table 2.  Modality-Specific Response and Remission Rates
Modality-Specific Response and Remission Rates
Table 3.  Meta-regression for Placebo Effect
Meta-regression for Placebo Effect
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Fraguas  D, Díaz-Caneja  CM, Pina-Camacho  L, Umbricht  D, Arango  C.  Predictors of placebo response in pharmacological clinical trials of negative symptoms in schizophrenia: a meta-regression analysis.   Schizophr Bull. 2019;45(1):57-68. doi:10.1093/schbul/sbx192 PubMedGoogle ScholarCrossref
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Nierenberg  AA, Østergaard  SD, Iovieno  N, Walker  RSW, Fava  M, Papakostas  GI.  Predictors of placebo response in bipolar depression.   Int Clin Psychopharmacol. 2015;30(2):59-66. doi:10.1097/YIC.0000000000000058 PubMedGoogle ScholarCrossref
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Meister  R, Jansen  A, Härter  M, Nestoriuc  Y, Kriston  L.  Placebo and nocebo reactions in randomized trials of pharmacological treatments for persistent depressive disorder: a meta-regression analysis.   J Affect Disord. 2017;215:288-298. doi:10.1016/j.jad.2017.03.024 PubMedGoogle ScholarCrossref
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Khan  A, Fahl Mar  K, Faucett  J, Khan Schilling  S, Brown  WA.  Has the rising placebo response impacted antidepressant clinical trial outcome? data from the US Food and Drug Administration, 1987-2013.   World Psychiatry. 2017;16(2):181-192. doi:10.1002/wps.20421 PubMedGoogle ScholarCrossref
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Furukawa  TA, Cipriani  A, Leucht  S,  et al.  Is placebo response in antidepressant trials rising or not? a reanalysis of datasets to conclude this long-lasting controversy.   Evid Based Ment Health. 2018;21(1):1-3. doi:10.1136/eb-2017-102827 PubMedGoogle ScholarCrossref
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Jilch  S, Sel  R, Shariat  SF.  Medical practice and placebo response: an inseparable bond?   Wien Klin Wochenschr. 2020;132(9-10):228-231. doi:10.1007/s00508-020-01626-9 PubMedGoogle ScholarCrossref
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George  MS, Lisanby  SH, Avery  D,  et al.  Daily left prefrontal transcranial magnetic stimulation therapy for major depressive disorder: a sham-controlled randomized trial.   Arch Gen Psychiatry. 2010;67(5):507-516. doi:10.1001/archgenpsychiatry.2010.46 PubMedGoogle ScholarCrossref
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2 Comments for this article
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Inappropriate Method Used to Measure the Placebo Effect
Caitlin Jones, PhD Candidate; Chris Maher, Professor and Director | The Institute for Musculoskeletal Health, The University of Sydney
We would like to express a concern with a recent systematic review which aimed to examine the magnitude of the placebo effect in trials for treatment resistant depression (1). The authors extracted baseline and follow-up depression scale scores within the placebo group, and conducted meta-analysis on these data. This method is not appropriate for the aims of the review because it does not measure the placebo effect.

The placebo effect can only be measured by comparing outcomes in the placebo arm to outcomes in the ‘no treatment’ arm (2). In this review, the within-group change of the placebo
arms has been erroneously attributed fully to the placebo effect. This is ignoring other explanations for within group change over time, such as natural history, regression to the mean and repeated testing. The mistake is analogous to judging the effect of a treatment by examining within group change over time. To measure placebo or treatment effects a control comparison is needed.

The large effect reported cannot be assumed to be purely due to placebo, therefore the conclusions of this review are largely overstated. A Cochrane review examined placebo effects in all conditions by looking at the difference between placebo and ‘no treatment’ arms, and found moderate quality evidence that placebo had a small and not statistically significant effect on depression (standard mean difference ‐0.25, 95% confidence interval ‐0.55 to 0.05) (2). Clinicians should be aware that while placebo arms have an important role to provide control and blinding in randomised controlled trials, they have limited clinical value (3).

References

1. Jones BDM, Razza LB, Weissman CR, Karbi J, Vine T, Mulsant LS, et al. Magnitude of the Placebo Response Across Treatment Modalities Used for Treatment-Resistant Depression in Adults: A Systematic Review and Meta-analysis. JAMA Network Open [Internet]. 2021 Sep 1 [cited 2021 Sep 25];4(9):e2125531–e2125531. Available from: https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2784479

2. Hróbjartsson A, Gøtzsche PC, Group CC and C. Placebo interventions for all clinical conditions. The Cochrane Database of Systematic Reviews [Internet]. 2010 [cited 2021 Sep 25];2010(1). Available from: /pmc/articles/PMC7156905/

3. Maher CG, Traeger AC, Abdel Shaheed C, O’Keeffe M. Placebos in clinical care: a suggestion beyond the evidence. The Medical Journal of Australia [Internet]. 2021 Sep 20 [cited 2021 Sep 26];215(6):252-253.e1. Available from: https://www.mja.com.au/journal/2021/215/6/placebos-clinical-care-suggestion-beyond-evidence

Caitlin Jones, PhD candidate at the Institute for Musculoskeletal Health
Professor Chris Maher, Director of the Institute for Musculoskeletal Health

CONFLICT OF INTEREST: None Reported
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Overinterpretation of Placebo-controlled Trials and Placebo Effects
Michael Hengartner, PhD | Zurich University of Applied Sciences, Switzerland
Throughout this manuscript Jones et al. confuse observed improvements in placebo groups with genuine placebo effects. The authors summarize their findings as "the placebo effect in TRD appears to be large and consistent across treatment modalities“ (1). This conclusion is not supported by the data and indicates a misinterpretation of placebo-controlled trials and genuine placebo effects. As detailed by Finniss et al, "the association of placebo effects with RCTs has caused confusion because the response in the placebo group is not necessarily a genuine psychosocial response to the simulation of treatment“ (2). The most conclusive research design to measure the magnitude of placebo effects is to compare a placebo intervention to no treatment assessed in the same healthcare setting (e.g., pill placebo plus clinical management compared to clinical management). Such studies provide no convincing evidence for robust and clinically relevant placebo effects in depression (3,4). Below I detail three main factors that may fully explain observed improvements in placebo groups.

First, the outcome in placebo groups is influenced by regression to the mean. This statistical phenomenon occurs when a group is selected based on high scores on a measurement that is subject to random error and then re-assessed with the same measurement (5). That is, patients are selected based on high depression scores that may be inflated due to random day-to-day fluctuations and random measurement error. These scores may not accurately reflect the patients’ true condition. When depression is re-assessed, measurement scores inevitably decline on the group level (3).

Second, treatment-resistant depression (TRD) is an ill-defined concept that often merely indicates that patients did not sufficiently respond to two different antidepressants (6). It does not follow that TRD is by necessity a chronic illness, and epidemiological research has repeatedly shown that depression is more likely to be recurrent that chronic (7, 8). Due to the episodic nature of depression, patients may improve substantially even when no treatment is provided, especially when they were selected into a study at a time when their condition was at its worst.

Third, patients randomized to placebo interventions may still benefit from frequent visits, close monitoring, psychosocial support, and the patient-clinician relationship. The therapeutic effects of these common treatment factors are well established in mental healthcare (9, 10). Additional treatments such as co-medication with benzodiazepines may also provide short-term benefits (3, 11).

In conclusion, Jones et al. misinterpret the observed outcome in placebo groups as genuine placebo effects. Placebo effects are only one among many factors influencing the outcome in placebo groups. There are many other factors, including regression to the mean, spontaneous remission, co-interventions, and common treatment factors, that may better explain improvements in measurement scores. Neglect of these results in  overestimation and overinterpretation of placebo effects. In depression and most other conditions, the effect of placebo interventions are small and uncertain rather than powerful and consistent (4, 12, 14).

References

1.         Jones BDM, Razza LB, Weissman CR, et al. Magnitude of the Placebo Response Across Treatment Modalities Used for Treatment-Resistant Depression in Adults: A Systematic Review and Meta-analysis. JAMA Netw Open. 2021;4(9):e2125531.

2.         Finniss DG, Kaptchuk TJ, Miller F, Benedetti F. Biological, clinical, and ethical advances of placebo effects. Lancet. 2010;375(9715):686-695.

3.         Hengartner MP. Is there a genuine placebo effect in acute depression treatments? A reassessment of regression to the mean and spontaneous remission. BMJ Evid Based Med. 2020;25(2):46-48.

4.         Hrobjartsson A, Gotzsche PC. Placebo interventions for all clinical conditions. Cochrane Database Syst Rev. 2010(1):CD003974.

5.         Barnett AG, van der Pols JC, Dobson AJ. Regression to the mean: what it is and how to deal with it. Int J Epidemiol. 2005;34(1):215-220.

6.         Fava GA, Cosci F, Guidi J, Rafanelli C. The Deceptive Manifestations of Treatment Resistance in Depression: A New Look at the Problem. Psychother Psychosom. 2020;89(5):265-273.

7.         Steinert C, Hofmann M, Kruse J, Leichsenring F. The prospective long-term course of adult depression in general practice and the community. A systematic literature review. J Affect Disord. 2014:65-75.

8.         Ten Have M, de Graaf R, van Dorsselaer S, Tuithof M, Kleinjan M, Penninx B. Recurrence and chronicity of major depressive disorder and their risk indicators in a population cohort. Acta Psychiatr Scand. 2018;137(6):503-515.

9.         Greenberg RP. The rebirth of psychosocial importance in a drug-filled world. Am Psychol. 2016;71(8):781-791.

10.       Lilienfeld SO, Ritschel LA, Lynn SJ, Cautin RL, Latzman RD. Why Ineffective Psychotherapies Appear to Work: A Taxonomy of Causes of Spurious Therapeutic Effectiveness. Perspect Psychol Sci. 2014;9(4):355-387.

11.       Ogawa Y, Takeshima N, Hayasaka Y, et al. Antidepressants plus benzodiazepines for adults with major depression. Cochrane Database Syst Rev. 2019;6:CD001026.

12.       Kienle GS, Kiene H. The powerful placebo effect: fact or fiction? J Clin Epidemiol. 1997;50(12):1311-1318.

13.       McDonald CJ, Mazzuca SA, McCabe GP, Jr. How much of the placebo 'effect' is really statistical regression? Stat Med. 1983;2(4):417-427.

14.       Maher CG, Traeger AC, Abdel Shaheed C, O'Keeffe M. Placebos in clinical care: a suggestion beyond the evidence. Med J Aust. 2021.

CONFLICT OF INTEREST: None Reported
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Original Investigation
Psychiatry
September 24, 2021

Magnitude of the Placebo Response Across Treatment Modalities Used for Treatment-Resistant Depression in Adults: A Systematic Review and Meta-analysis

Author Affiliations
  • 1Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
  • 2Department of Internal Medicine, Faculty of Medicine, University of Sao Paulo, Sao Paulo, Brazil
  • 3Laboratory of Neurosciences, Instituto Nacional de Biomarcadores em Neuropsiquiatria, Department and Institute of Psychiatry, Faculdade de Medicina, University of Sao Paulo, Sao Paulo, Brazil
  • 4Centre for Addiction and Mental Health, Toronto, Ontario, Canada
  • 5Michael G. Degroote School of Medicine, McMaster University, Hamilton, Ontario, Canada
  • 6Nova Scotia Health, Halifax, Nova Scotia, Canada
  • 7Department of Psychiatry, University of California San Diego
JAMA Netw Open. 2021;4(9):e2125531. doi:10.1001/jamanetworkopen.2021.25531
Key Points

Question  What is the placebo effect magnitude in different treatment modalities used for management of patients with treatment-resistant depression?

Findings  In this systematic review and meta-analysis of 3228 patients with treatment-resistant depression in 50 randomized clinical trials, the placebo effect size was large and consistent across treatment modalities. Response and remission rates associated with placebo effect were comparable across modalities.

Meaning  The findings of this study suggest a placebo effect size benchmark may be used to interpret the findings of past and future clinical trials.

Abstract

Importance  The placebo effect in depression clinical trials is a substantial factor associated with failure to establish efficacy of novel and repurposed treatments. However, the magnitude of the placebo effect and whether it differs across treatment modalities in treatment-resistant depression (TRD) is unclear.

Objective  To examine the magnitude of the placebo effect in patients with TRD across different treatment modalities and its possible moderators.

Data Sources  Searches were conducted on MEDLINE, Web of Science, and PsychInfo from inception to June 21, 2021.

Study Selection  Randomized clinical trials (RCTs) were included if they recruited patients with TRD and randomized them to a placebo or sham arm and a pharmacotherapy, brain stimulation, or psychotherapy arm.

Data Extraction and Synthesis  Independent reviewers used standard forms for data extraction and quality assessment. Random-effects analyses and standard pairwise meta-analyses were performed.

Main Outcomes and Measures  The primary outcome was the Hedges g value for the reported depression scales. Secondary outcomes included moderators assessed via meta-regression and response and remission rates. Heterogeneity was assessed with the I2 test, and publication bias was evaluated using the Egger test and a funnel plot. Cochrane Risk of Bias Tool was used to estimate risks.

Results  Fifty RCTs were included involving various types of placebo or sham interventions with a total of 3228 participants (mean [SD] age, 45.8 [6.0] years; 1769 [54.8%] female). The pooled placebo effect size for all modalities was large (g = 1.05; 95% CI, 0.91-1.1); the placebo effect size in RCTs of specific treatment modalities did not significantly differ. Similarly, response and remission rates associated with placebo were comparable across modalities. Heterogeneity was large. Three variables were associated with a larger placebo effect size: open-label prospective treatment before double-blind placebo randomization (β = 0.35; 95% CI, 0.11 to 0.59; P = .004), later year of publication (β = 0.03; 95% CI, 0.003 to 0.05; P = .03), and industry-sponsored trials (β = 0.34; 95% CI, 0.09 to 0.58; P = .007). The number of failed interventions was associated with the probability a smaller placebo effect size (β = −0.12; 95% CI, −0.23 to −0.01, P = .03). The Egger test result was not significant for small studies’ effects.

Conclusions and Relevance  This analysis may provide a benchmark for past and future clinical RCTs that recruit patients with TRD standardizing an expected placebo effect size.

Introduction

Major depressive disorder (MDD) is a relapsing and remitting illness, and many patients do not respond to available treatments.1,2 The standard to evaluate a new intervention for MDD uses a randomized clinical trial (RCT) with placebo as the control design that can distinguish the benefit of an active treatment compared with the nonspecific benefit of the placebo response. The placebo response is defined as the therapeutic effect produced by a placebo or sham intervention that is not due to any inherent properties of the placebo. Several novel or repurposed treatments have not been able to establish efficacy in the context of large placebo effects.3 This phenomenon is a challenge for researchers; however, attention has begun to focus on trying to understand and quantify the magnitude of the placebo response.1,2

In RCTs of non–treatment-resistant depression (non-TRD) in patients with MDD,4-14 the placebo effect has been found to have a large magnitude and to be associated with several factors, although with some inconsistent findings. These factors have included later publication years, number of trial arms, multicenter setting, dosing schedule, increased length of the trial, sham device placement, the magnitude of active response, early score fluctuations, and inflation of baseline severity.4-14 A large meta-analysis evaluating the placebo effect in depression (256 RCTs; n = 26 324) found placebo-response rates of about 35% to 40%.13 However, this analysis included only RCTs of antidepressant drugs in patients without TRD.13 Treatment-resistant depression is commonly defined as the lack of response to 2 separate antidepressant trials of adequate dose and duration. Previous meta-analyses have suggested that TRD is associated with a smaller placebo effect than non-TRD (repetitive transcranial magnetic stimulation [rTMS] and escitalopram trials).15,16 Nonetheless, to our knowledge, no analysis has assessed the placebo effect in patients with TRD receiving other treatment modalities.

Patients with TRD often receive multiple treatment modalities and have lower rates of response and remission; thus, one would expect them to experience less benefit from the nonspecific effects of treatment. This lack of response highlights the importance of quantifying the placebo effect in TRD, how it may differ across treatment modalities, and what may contribute to it. An appreciation of the expected placebo effect of specific treatment modalities in TRD would provide a benchmark to inform interpretation of past and future RCTs. We therefore conducted a systematic review and meta-analysis to quantify the placebo effect across treatment modalities in TRD. We explored the methodological, demographic, and clinical variables that may contribute to placebo effect. This quantitative analysis is needed to interpret previous RCTs and emerging treatments as well as potentially identifying the beneficial aspect of the placebo effect.

Methods

Searches were conducted on MEDLINE, Web of Science, and PsychInfo from inception to June 21, 2021 (eAppendix 2 in the Supplement). In addition, references from relevant reviews were searched.15,17-21 Figure 1 provides an overview of the number of studies screened and full texts reviewed. This study followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guideline. The protocol was developed a priori and was published and registered22,23 (trial registration: PROSPERO Identifier: CRD42020190465).

Study designs were limited to parallel-arm, double-blind placebo-controlled RCTs or the first phase of randomized crossover trials that exclusively recruited patients with TRD and randomized them to at least 1 placebo arm, as specified in the protocol.22 Included active interventions were pharmacologic and somatic treatments (Maudsley Treatment Inventory) and psychotherapeutic interventions from the National Institute for Health and Care Excellence (NICE) guidelines.24,25 A placebo arm was defined as an inert pill placebo, parenteral placebo, sham device, or sham therapy that does not include any theoretical active property to induce the proposed therapeutic effect. Wait list or treatment as usual were not included as placebo interventions. Randomized clinical trials that used an open-label prospective antidepressant treatment phase to establish treatment resistance before randomization were included. Exclusion criteria were studies in which more than 15% of participants had bipolar depression, included patients with other psychiatric disorders, had sample sizes of less than 10 participants (5 per treatment arm), or used a noninert placebo arm.

Data were extracted by 3 of us (L.B.R., J.K., L.S.M.) and discrepancies were resolved by consensus. Data were confirmed by 1 of us (B.D.M.J.). Variables extracted included demographic characteristics, methodologic data, clinical/outcome data, and placebo-specific data. The demographic characteristics were age and sex. Methodological data included treatment strategy (augmentation vs monotherapy), multicenter setting, length of the trial (calculated as number of days for double-blind treatment), number of treatment arms, year of publication, trial design (ie, whether the trial used open-label prospective treatment to determine treatment resistance before double-blind randomization), and whether the trial was industry sponsored (defined as being sponsored by a pharmaceutical company or a medical device manufacturer). Randomized clinical trials funded by the US Department of Veterans Affairs; government agencies, such as the Canadian Institutes of Health Research or National Institute of Mental Health; nonprofit foundations, such as National Alliance for Research on Schizophrenia & Depression, or Brain & Behavior Research Foundation; or hospitals or universities were not considered to be industry sponsored. Clinical/outcome data comprised treatment modality, sample size, mean (SD) scores of depression rating scales at baseline and end point in both the placebo and active groups, number of responders/remitters (as defined by the individual studies), number of failed antidepressant trials, past depressive episodes, and length of the current episode. Placebo-specific data included route (eg, oral vs parenteral) of administration and number of days receiving placebo. This variable was calculated by determining how many days during the trial period participants would receive placebo or sham intervention.

If the mean (SD) scores of the depression rating scales were not reported in the study, the corresponding author was contacted by email. If no reply was obtained, we extracted the data from the study graphs. Moreover, when only the SD of the change was reported, we imputed the data as recommended by the Cochrane group.26 For studies that included an open-label prospective (run-in) treatment phase, effect size was calculated from the start of the double-blind randomized phase.

The quality of the studies was assessed independently by 2 of us (B.D.M.J. and C.R.W.) and recorded using the Cochrane Risk of Bias tool.27 Thus, 5 bias domains were assessed: selection (randomization and allocation concealment), performance (blinding of participants and personnel), attrition (incomplete outcome data), detection (blinding of outcome assessment), and reporting (selective outcome reporting). For a judgment of the overall risk of bias, we followed the Cochrane recommendation: low risk of bias (low risk for all domains or some concerns of bias for 1 domain), unclear risk of bias (2 or 3 domains were rated as presenting some cause for concern), and high risk of bias (>3 domains with some bias concerns and/or ≥1domain with high risk of bias).

The analyses were performed using Stata, version 17 (StataCorp LLC) software. Considering that most studies have small samples, we used the Hedges g to estimate the effect size of the placebo response. The effect size was computed based on the baseline and end point sample sizes and means (SDs) of the primary depression score scales of the placebo group of each study. A random-effects model (restricted maximum-likelihood method instead of fixed-effect models was used considering that study heterogeneity would be high). The model provides wider 95% CIs compared with fixed-effects models, thereby providing a more conservative estimation of summary effect size.28 The model assumes that the effect sizes of the studies are different from each other and represent a random sample from a larger population of studies.29 The components of the random-effects model applied in this meta-analysis are described in eAppendix 1 in the Supplement. Heterogeneity among studies was assessed with I2 analysis and was considered high when presenting a value greater than or equal to 50% as suggested elsewhere.30 Small studies effects were assessed using the Begg modified funnel plot, the Duval and Tweedie trim-and-fill procedure, and the Egger regression intercept. The Egger test was considered significant for small studies’ effects when presenting findings significant at P < .05.

Meta-regression and subgroup analyses were applied to explore potential moderators of the placebo effect. To increase overall sample size, treatment modalities were pooled for the meta-regression. Meta-regressions were conducted using only 1 variable at a time. Subgroup analysis was conducted to compare the effect size of the placebo response among treatment modalities. Values were considered statistically significant when presenting findings at P < .05 for both meta-regression and subgroup analysis. The mean of the percentage of responders and remitters for each treatment modality was also assessed. In addition, a sensitivity analysis was conducted looking at low risk of bias.

Results

Our search yielded 11 236 studies that were screened; full texts of 605 articles were reviewed and 50 RCTs (N = 3228) were included.31-80 the mean (SD) age of the participants was 45.8 (6.0) years, and the mean (SD) proportion of women was 54.8% (20.7%) (Figure 1 and Table 1). Additional clinical characteristics are described in eTable 3 in the Supplement. Sixteen RCTs were classified to have a low risk of bias; 19, an unclear risk of bias, and 16, a high risk (eTable 1 in the Supplement). No psychotherapy RCTs met our eligibility criteria. Placebo or sham interventions were categorized as pill placebo, liquid placebo (trials in this analysis used ayahuasca, a South American psychoactive brew), parenteral placebo, sham rTMS, sham transcranial direct current stimulation (tDCS), or sham invasive brain stimulation.

Studies were pooled based on treatment modality to maintain sufficient similarity in the pairwise analysis. The effect sizes for each treatment modality were pill placebo, g = 1.14 (95% CI, 0.99 to 1.30; I2 =  80.25%); parenteral placebo, g = 1.33 (95% CI, 0.63 to 2.04; I2 = 62.28%); liquid placebo, g = 0.45 (95% CI, −0.26 to 1.15; I2 = 0%); rTMS, g = 0.89 (95% CI, 0.63 to 1.15; I2 = 62.14%); tDCS, g = 1.32 (95% CI, 0.53 to 2.11; I2 = 52.57%); and invasive brain stimulation, g = 0.86 (95% CI, 0.58 to 1.14; I2 = 15.48%). The pooled effect size for all treatment modalities was g = 1.05 (95% CI, 0.91 to 1.18) with an overall I2 = 76.19% (Figure 2 and Figure 3).

The funnel plot of the primary outcome showed a symmetrical distribution among the included studies (eFigure 1 in the Supplement). The Egger test corroborated this finding (z = 1.18; P = .23). We also applied the trim-and-fill method to deeply investigate any asymmetry of the funnel plot (eFigure 2 in the Supplement) and its analysis showed the presence of 5 unpublished studies. Considering these studies in the pooled analyses, the overall effect size was adjusted to g = 1.14 (95% CI, 1.00 to 1.29). The overall placebo effect size remained large when considering only studies with a low risk of bias (g = 1.18; 95% CI, 0.98 to 1.38) (eFigure 3 in the Supplement).

For a subset of RCTs that reported response rates (n = 42) and remission rates (n = 25), the pooled mean (SD) response rate was 21.2% (14.6%) and the pooled remission rate was 13.0% (9.05%). Modality-specific response and remission rates are described in Table 2.

Pooled meta-regression analysis revealed that industry-sponsored studies (β = 0.34; 95% CI, 0.09 to 0.59; P = .007), year of publication (β = 0.03; 95% CI, 0.003 to 0.05; P = .03), and studies that used an open-label prospective treatment phase before double-blind randomization (β = 0.35; 95% CI, 0.11 to 0.59; P = .004) had a significantly higher placebo effect. The number of failed interventions during the current episode was associated with a smaller placebo effect (β = −0.12; 95% CI, −0.23 to −0.01; P = .03) (Table 3).

Subgroup analysis found no significant differences in placebo effect among treatment modalities (eTable 2 in the Supplement). Owing to the high heterogeneity between studies, no other subgroup analysis was completed.

Discussion

In this meta-analysis, we report on the placebo effect in TRD across multiple treatment modalities. We synthesized data from 50 RCTs including 3228 participants who received either pill placebo, parenteral placebo, liquid placebo, sham rTMS, sham tDCS, or sham invasive brain stimulation. The combined placebo effect size for all interventions was large (g = 1.05) and the placebo effect sizes for each treatment modality did not significantly differ. This finding is consistent with prior analyses that have shown that placebo in MDD (non-TRD) has a large effect size, seen with RCTs of antidepressants (Cohen d = 1.69), rTMS (g = 0.8), or tDCS (g = 1.0914,15,81). Studies in other psychiatric populations have also shown a high placebo effect size, such as negative symptoms in schizophrenia (Cohen d = 2.91) and response rate (39.2%) in bipolar depression.82,83 Although all of these analyses reported a large effect size, the effect size found in RCTs involving patients with TRD is numerically smaller for pill placebo and numerically larger for sham stimulation than the effect sizes reported in these RCTs involving patients without TRD.14,15,81

In our secondary analysis, we assessed response and remission rates as reported in the individual RCTs. The pooled response rate across all treatment modalities was 23.5% and the remission rate was 15.5%. These low rates did not differ significantly across treatment modalities. A large meta-analysis in patients without TRD reported a response rate of placebo to be 35% to 40%, which is numerically higher than our rate of 21.2%.13 Another meta-analysis reported a remission rate of 22%, which is numerically higher than our finding of 13.0%.84 Earlier studies of placebo effect that have included participants with TRD have found that TRD was associated with a lower placebo response, which may explain why our response and remission rates appear to be lower than in studies involving patients without TRD.15

Our meta-regression found that RCTs that used an open-label prospective treatment phase before double-blind randomization, a more recent year of publication, and those that were industry sponsored had a larger placebo effect. A meta-analysis of placebo response in negative symptoms of schizophrenia also reported that placebo response was higher in industry-sponsored clinical trials (Cohen d = 6.72) compared with academic-funded trials (Cohen d = 1.01).82 Only pill placebo RCTs used an open-label prospective treatment phase and only 1 small rTMS RCT was industry sponsored. Previous research has also suggested that, in populations without treatment-resistant MDD, a higher placebo response was associated with more recent year of publication.85 Other studies have suggested that finding may reflect methodological changes, such as an increased number of multicenter studies.86 Placebo effect has been shown to be associated with expectancy of active treatment and increased activity in reward circuitry.6,87 Industry-sponsored RCTs are often investigating novel agents, which may lead to increased expectations of efficacy by participants, although we are unable to confirm this possibility with available data. Another potential contributing factor to the higher placebo effect in the prospective open-label treatment trials is that of delayed antidepressant response. In these studies, patients are continuing the recently started antidepressant from the prospective open-label treatment phase in combination with placebo or another active agent, which may be contributing to a delayed response. The number of failed trials was also significantly associated with a smaller placebo effect. Although only 17 of 50 studies reported on this variable, this finding is consistent with previous literature showing that a higher level of treatment resistance is associated with a smaller placebo effect.15

Our analysis did not find that the placebo effect was significantly different across treatment modalities. This finding is consistent with trials comparing rTMS with escitalopram.16 The large placebo effect in TRD does not change substantially based on modality. This lack of effect supports the notion that the nonspecific factors contributing to the placebo effect in TRD are prevalent in any clinical trial. Our findings suggest that the invasiveness of the intervention (eg, pill vs invasive brain stimulation) does not substantially affect the placebo effect in TRD. Given the consistency of the placebo effect across treatment modalities in TRD, neurobiological and common psychological factors need to be further investigated.

Our primary finding that the placebo effect in TRD is large (g = 1.05) and that it is consistent across treatment modalities may help to interpret the results of past and future RCTs. Researchers who conduct clinical trials may now compare their results with a benchmark for expected placebo effect in TRD. For instance, a placebo-controlled RCT that reports negative findings but had a placebo effect size greater than g = 1.05 could be interpreted as a false-negative; conversely, an RCT that reports positive findings with a placebo effect size less than g = 1.05 could be a false-positive. Our results also provide a context for noninferiority trials that do not use a placebo or sham control intervention. Treatments that differentiate from this benchmark (g = 1.05) by a predetermined margin would be expected to be superior to placebo.

Limitations

This study has limitations. Although we were able to include several different treatment modalities in our analysis, no psychotherapy RCTs met our eligibility criteria. Psychotherapy trials in TRD were either open-label, single arm, had an active comparator, or used treatment as usual or a wait-list condition. There were also no RCTs of electroconvulsive therapy or magnetic seizure therapy that used sham versions of these procedures in a TRD population. Another limitation is the definition of TRD. Although we used the most common definition, there is no standard TRD definition. A large proportion of studies that were excluded reported including patients with TRD even though they defined TRD based on only one failed antidepressant trial. For this reason, we were unable to include 2 large industry-sponsored trials for rTMS.88,89 Future research would benefit from an improved and consistent definition of TRD. Current definitions of TRD are homogeneous and do not consistently account for a number of important factors, such as specific treatments failed (eg, psychotherapy vs brain stimulation vs selective serotonin reuptake inhibitor vs serotonin and norepinephrine reuptake inhibitor) and psychosocial factors. Although we aimed to compare the placebo effect across treatment modalities, our results are limited by the absence of direct comparison of 2 placebo modalities (ie, sham brain stimulation vs pill placebo).

Conclusions

The present systematic review and meta-analysis compared the placebo effect in TRD across different treatment modalities. Our main finding was that the placebo effect in TRD appears to be large and consistent across treatment modalities. The effect size in the studies included in our analysis (g = 1.05) may serve as a benchmark to assess placebo effect in future TRD RCTs. Factors that increase the placebo effect appear to include using an open-label, prospective treatment phase and industry sponsorship. To better understand the placebo effect, the following improvements are needed: more consistent reporting of data, an agreement on a standard definition of TRD and its possible subgroups, and further assessment and reporting of participants’ expectations and experiences within a clinical trial.

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Article Information

Accepted for Publication: July 15, 2021.

Published: September 24, 2021. doi:10.1001/jamanetworkopen.2021.25531

Correction: This article was corrected on December 30, 2021, to fix label errors in Figure 2 and Figure 3.

Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2021 Jones BDM et al. JAMA Network Open.

Corresponding Author: Zafiris J. Daskalakis, MD, PhD, Department of Psychiatry, University of California San Diego, 9500 Gilman Dr, La Jolla, CA 92093 (zdaskalakis@health.ucsd.edu).

Author Contributions: Dr Jones and Ms Razza are co-first authors and had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Jones, Weissman, Brunoni, Husain, Daskalakis.

Acquisition, analysis, or interpretation of data: Jones, Razza, Weissman, Karbi, Vine, L. Mulsant, B. Mulsant, Blumberger, Daskalakis.

Drafting of the manuscript: Jones, Weissman, Husain, Daskalakis.

Critical revision of the manuscript for important intellectual content: Jones, Razza, Weissman, Karbi, Vine, L. Mulsant, Brunoni, Husain, B. Mulsant, Blumberger.

Statistical analysis: Jones, Razza, Brunoni, Daskalakis.

Obtained funding: Jones, Daskalakis.

Administrative, technical, or material support: Jones, Weissman, Daskalakis.

Supervision: Jones, Weissman, Brunoni, Husain, B. Mulsant, Blumberger, Daskalakis.

Conflict of Interest Disclosures: Dr Blumberger has received research support from the Canadian Institutes of Health Research, the National Institutes of Health (NIH), Brain Canada, and the Temerty Family through the Centre for Addiction and Mental Health (CAMH) Foundation and the Campbell Research Institute. He received research support and in-kind equipment support for an investigator-initiated study from Brainsway Ltd, and he is the site principal investigator for 3 sponsor-initiated studies for Brainsway Ltd. He also receives in-kind equipment support from Magventure for investigator-initiated studies. He received medication supplies for an investigator-initiated trial from Indivior. Dr Mulsant holds and receives support from the Labatt Family Chair in Biology of Depression in Late-Life Adults at the University of Toronto; he also currently receives research support from Brain Canada, the Canadian Institutes of Health Research, the CAMH Foundation, the Patient-Centered Outcomes Research Institute (PCORI), the NIH, Capital Solution Design LLC (software used in a study funded by the CAMH Foundation), and HAPPYneuron (software used in a study founded by Brain Canada). Within the past 5 years he has also received research support (medications or matching placebo pills for NIH-funded clinical trials) from Bristol-Myers Squibb, Eli Lilly, and Pfizer. Dr Brunoni receives grants from the National Council for Scientific and Technological Development (CNPQ, PQ-1B), the Program of Academic Productivity of the University of São Paulo Medical School, and from the São Paulo Research Foudation. Dr Brunoni is chief medical advisor of Flow and has a small equity of the company. The LIM-27 Laboratory of Neuroscience receives grants from the Associação Beneficente Alzira Denise Hertzog da Silva. Dr Husain reports receiving grants from Stanley Medical Research Institute during the conduct of the study, personal fees from COMPASS Pathways Limited, grants from the PSI Foundation, and grants from Brain and Behavior Research Foundation outside the submitted work, and was previously appointed a member of the board of trustees of Pakistan Institute of Living and Learning. Ms Razza is supported by São Paulo Research Foundation. Dr Husain reported grants from COMPASS Pathways Limited, grants from University of Toronto, grants from PSI Foundation, grants from Brain and Behavior Research Foundation, and grants from CAMH Foundation outside the submitted work. Dr B. Mulsant reported he holds, and receives support from, the Labatt Family Chair in Biology of Depression in Late-Life Adults at the University of Toronto. He currently receives research support from Brain Canada, the Canadian Institutes of Health Research, the CAMH Foundation, PCORI, the US NIH, Capital Solution Design LLC (software used in a study founded by CAMH Foundation), and HAPPYneuron (software used in a study founded by Brain Canada). Within the past 3 years, he has also received research support from Eli Lilly (medications for an NIH-funded clinical trial) and Pfizer (medications for an NIH-funded clinical trial). He has been an unpaid consultant to Myriad Neuroscience. Dr Blumberger reported nonfinancial support from Magventure, in-kind equipment support for investigator-initiated research, other support from Brainsway Site PI for sponsored clinical trials, and other support from Janssen One advisory board meeting outside the submitted work. Dr Daskalakis reported grants from Brainsway Inc, nonfinancial support from Magventure Inc, and nonfinancial support from Brainsway during the conduct of the study. No other disclosures were reported.

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