Rationale

Psilocybin is a potentially paradigm-shifting depression intervention. We conducted a systematic review and meta-analysis of psilocybin-for-depression randomized controlled trials (RCTs).

Objectives

Systematically assess harm reporting, risk of bias, action mechanism specification, and incremental therapeutic effect sizes in the psilocybin-for-depression RCT literature.

Methods

Assessed databases included PsycINFO, CINAHL, Embase, Medline, Web of Science, and Scopus. Search terms “Psilocybin” or “Psychedelic” were paired with “Depression”, and "Randomized Controlled Trial" or “RCT”.

Results

We identifiedk = 9 RCTs (k = 10 subgroups) involvingn = 602 participants (56% psilocybin). Five studies had low/very low harm quality reporting, opposed to two with high. Most studies demonstrated a high risk of bias. Therapeutic mechanisms of action (MoAs) were discussed in varying detail but rarely assessed in original publications. Psilocybin was moderately superior to controls at reducing depression (g = 0.62; 95% CI = 0.27, 0.98). Effects were heterogenous (τ = .47). Smaller studies evidenced stronger effects that favored psilocybin (Egger’sb0 = 3.63,p = .014). Almost all studies documented financial conflicts of interests.

Conclusion

Psilocybin demonstrates significant depression reduction relative to controls. However, researchers, clinicians, and stakeholders should consider several contextual factors. Effects were moderate and attenuated in larger and better-controlled studies. Harms reporting and risk of bias was high, though partly driven by unique challenges of psilocybin research. MoAs were variably specified but rarely assessed; suggesting it is unclear how depression is reduced. We advise researchers conduct RCTs with active control conditions, larger samples, and include MoA assessments. Independent RCTs from researchers without financial conflicts of interest are needed.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00213-025-06788-w.

Keywords: Depression, Psychedelics, Psilocybin, Randomized Controlled Trial, Harm Reporting, Risk of Bias, Effect Size, Mechanisms of Action

Search strategy

The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA; Page et al.2021) guidelines were followed for this study (see Fig. 1 for study selection flowchart). The databases used to gather the articles were PsycINFO, CINAHL, Embase, Medline, Web of Science, and Scopus. Search terms “Psilocybin” or “Psychedelic” were paired with “Depression”, and "Randomized Controlled Trial" or “RCT”. Search terms were identical per search engine. The search included all published research through March 2024^a.

Inclusion/Exclusion Criteria

The following inclusion criteria were utilized: a) human subjects research, b) psilocybin administration, c) inclusion of participants in a control condition, d) inclusion of at least one validated measure of depression, e) RCT design component (cross-over designs were included for data gathered prior to the cross over), and f) sufficient information available to extract effect sizes (pre/post between-subjectsn’s, means, and standard deviations).

Exclusion criteria were a) secondary analyses of original RCT data, b) non-peer review publication outlet, and c) psilocybin only control conditions (i.e., dosage studies). English language publication wasnot an exclusion criterion.

Data extraction and study selection criteria

After completing the initial literature search, titles and abstracts were assessed. If the abstract suggested the study would meet inclusion criteria, the full text was reviewed by a team member. Studies that met inclusion criteria had means (mean change when raw means were not available), standard deviations/errors,n’s, therapeutic and pharmacological mechanisms, and basic demographic information extracted for analyses. Any extraction/coding issues were resolved via group discussion. When data were unclear or unavailable, corresponding authors were emailed. Consistent with Yu et al. (2022), if we could not obtain specific estimates, we extracted estimates from figures using the Web Plot Digitizer tool (version 4) to estimate means and deviation estimates (Automeris LLC.2024). We also tested the Web Plot Digitizer tool on our charts and observed the tool to be sufficiently accurate (largest error observed was within 0.02). To minimize researcher bias, the first author did not provide input on coding/extraction decisions besides determining initial criteria.

Harm reporting

We followed previous review work (Taillefer de Laportalière et al.2023) by using the 21-item adverse reporting checklist (Ioannidis et al.2004; Taillefer de Laportalière et al.2023) informed by the Consolidated Standards of Reporting Trials (CONSORT) Extension of Harms checklist (Ioannidis et al.2004). Questions were evaluated on a binary scale where “1” indicates they met criteria. Coding was conducted by two trained research assistants, then replicated by two co-authors (TO and JV), then re-evaluated by an additional team members selected from the broader authorship list. Discrepancies were resolved through team discussion. Specific in-text evidence for a score of “1” for each criterion was extracted to aid in replication. Additionally, excerpts were extracted for evidence of partial criteria (these were still scored as “0” if full criteria were not met). Only original manuscript materials were considered during harm reporting review (i.e., supplemental materials were excluded). Scores for each item on the checklist were summed and described as high quality (17–21); moderate quality (12–16); low quality (7–11); or very low quality (0-6; Taillefer de Laportalière et al.2023).

Risk of bias

Risk of bias was assessed via the Cochrane’s Risk of Bias 2.0 (Higgins et al.2019; Sterne et al.2019). RoB 2.0 is the recommended tool for evaluating likelihood of bias in RCTs. It consists of five risk domains (risk from the randomization process, deviations from the intended intervention, missing outcome data, measurement of the outcome, and reported results), each domain has multiple questions that are measured on a four-level metric from yes to no with a “no information provided” option. The risk of bias for each domain is qualitatively described as low, medium, and high with an overall risk of bias for the study’s effect. The risk of bias by domain and by the study are determined by an algorithm (Sterne et al.2019) with question-level metrics informing domain metrics, which in turn inform study metrics. However, despite the algorithm being provided by the tool creators, it is appropriate to override the estimate of bias in instances where a particular risk of bias does not appear to be of concern. Or, conversely, when an estimate of a particular risk of bias is concerning, but not detected by the algorithm. Two coauthors (JV and DP) independently conducted risk of bias assessment using the RoB 2.0 tool. Initial interrater reliability for RoB coding was Cohen’sκ = 0.57 (moderate agreement). Rating of “probable yes” and “probable no” were converged with their respective “yes” and “no” classifications. After discussion and re- review, 100% agreement was achieved.

Action mechanisms synthesis

We completed a review of all posited therapeutic MoAs reported in the reviewed RCTs. One co-author (DP) reviewed the introduction and discussion sections of each manuscript, extracted the original author wordings that were used to describe how psilocybin reduced depression. Separate classifications/appraisals were afforded for neurophysiological (e.g., neurotransmitter mechanisms) and psychological mechanisms (e.g., spirituality mechanisms). Co-authors (TO and JV) then independently reviewed and coded each RCT as either "Highly Specific", "Moderately Specific", “Vague”, or "NA" based on each manuscript's level of detail of the extracted MoA(s). The highest codes received by a study’s MoA description became the study’s overall classification for that type of mechanism. Discrepancies between the reviewers were settled by team discussion between DP, JV, and TO.

Incremental efficacy data analysis

Effect sizes were analyzed using Comprehensive Meta-Analysis (v4) software (Borenstein et al.2013). All analyses were modeled under random effects. Hedges’g was selected as the index of effect size to adjust for the relatively lown’s observed across many of the study conditions. Post-score standard deviation was used to standardize the effect sizes, this was chosen instead of pre-post correlations due to a lack of reporting of pre-post correlations. In all analyses, we examined theincremental effect sizes of psilocybin intervention relative to control. That is, we compared the within group change across timepoints between the groups. Incremental Hedges’g represents the strength of the treatment group (psilocybin) relative the control in reducing depression in standard deviation units. Consistent with (Pizer et al.2024), we adopted Ferguson’s (2009) recommendation of Hedges’g ≥ 0.41 as the threshold for minimal “practical” significance.

Indicators were coded such that positive values represented a greater therapeutic effect in the psilocybin conditions relative to controls (greater depressionreduction). We conducted meta-analyses where all psilocybin conditions were compared to a novel control condition with time points and depression measures nested within study subgroups. Study subgroups were set as the unit of analysis. Publication bias was statistically assessed using Egger’s regression test, which regresses the effect sizes on the inverse of the standard error. Duval and Tweedie’s trim-and-fill method was used to assess the possibility of missing studies due to publication bias (Duval & Tweedie2000). Tau (τ) estimates were also calculated to provide heterogeneity context.

Data availability

CMA data sheets are available on the Open Science Framework https://osf.io/p3fcx/.

Table 1.

Harm reporting

Table2 breaks down harm reporting criteria by each RCT/criterion. Quality was heterogenous. Over half (k = 6) the studies were classified as low or very low, one was classified as moderate, and two as high. Criterion 4d was universally missed (i.e.,Described the plan for monitoring for harms and rules for stopping the trial because of harms). Almost all studies missed criteria 2 (“Information on AEs mentioned in the introduction”), 3a (“Definitions of AEs mentioned”), 3b (“If article mentioned all or selected sample of AE”), 3c (“If article mentioned the use of a validated instrument to report AEs severity”), 4c (Description of how AE were attributed to trial drugs”), 5b (“Description of approach for the handling of recurrent AEs”), 7a (“Provided denominators for AEs”), 8a (“Reported results separately for each treatment arm”), 8c (“Provided both number of AEs and number of patients with AEs”), and 9 (“Described subgroup analysis and exploratory analysis for harms”). Criteria 4b (“Stated the timing of collection of AE data”) and 6b (“Reported deaths and serious AEs”) were almost always met. Marschall et al. (2022) had the most missed scores by a substantial margin. Goodwin et al. (2022) and Raison et al., (2023) had the best harm quality reporting. For a list of specific appraisal criteria see Supplementary File2. Extracted excerpts from each reviewed manuscript are also provided in Supplementary Files3–11. We also provide excerpts of when criteria were partially fulfilled, such as in the case of meeting one barrel of a double-barreled question (e.g., criteria 8b “Severityand grading of AEs”), and of when authors provided indirect harm reporting evidence (which was still considered insufficient for a favorable score, but could conceivably be linked to harm reporting).

Risk of bias

Outcomes from the risk of bias review are available in Table 3. Full results including specific RCT-by-criteria rating are available in the Supplementary File12. RoB 2.0 algorithm results suggested “High” overall bias across all RCTs. Reviewer subjective appraisal concurred with the algorithm that overall risk of bias was high for six studies, though diverged from the algorithm for Back et al. (2024), Goodwin et al. (2022), and Marschall et al. (2022) which we thought would be better classified as having “Some Concerns”. Reasons for bias scores were largely clustered around themeasurement of the outcome domain. Otherwise, risk of bias was relatively heterogenous across domains/RCTs. Rosenblat et al. (2024) was determined to have the highest risk, being classified as “High” by both the algorithm and assessors across three different domains. Whereas Goodwin et al. (2022) generally evidenced the lowest risk of bias.

Action mechanisms

Table4 illustrates results from the MoA synthesis. Overall, most RCTs provided at least some level of description for neurophysiological and/or psychological MoAs. Ross et al. (2016) and Marschall et al. (2022) were the only studies classified as “Highly Specific” in both domains. Three studies did not specify any neurophysiological mechanisms (Goodwin et al.2022; Raison et al.2023; Rosenblat et al.2024), while Raison et al. (2023) was the only study not to specify a psychological mechanism. For a comprehensive list of extracted MoA statements with accompanying classification, see Supplementary File13. The most commonly mentioned MoA was action on 5- HT_2A, though detail regarding how 5-HT_2A functioned in relation to depression reduction was variable. Additionally, MoA statements tended to occur in discussion sections (k = 24 extracted MoA statements), relative to introductions (k = 15;k = 6 of which came from Marschall et al.2022).

Incremental efficacy meta-analyses

Random effects meta-analysis of all available effect sizes of psilocybin intervention relative to control conditions suggested a significant incremental effect favoring psilocybin as being statistically superior at reducing depression symptoms,g = 0.69; 95% CI = 0.34, 1.04. The results were heterogenous τ = 0.47,Q(9) = 39.25,p < 0.001, see Fig. 2 for a forest plot of effect sizes. Given the heterogeneity, we provide omnibus results but recommend evaluating each incremental effect sizes to ascertain a comprehensive reflection of the research body. Table5 provides the incremental effects by study subgroup with relative weights. Supplementary File14 provides a breakdown of each incremental effect sizes across all RCTs (note that the individual incremental effects in Supplementary File14 are not adjusted by study-level dependence). Omnibus publication bias estimates indicated a significant correlation between Hedges’g and standard error (Egger’sb0 = 3.64,SE = 1.36,t[8] = 2.68,p = 0.014), such that smaller studies tended to yield larger effects that favored psilocybin. The trim-and-fill procedure removed one study, giving an adjusted incremental g = 0.56 95% CI = 0.16, 0.95. Subgroup-analyses indicated the effect was driven by studies where psilocybin was compared against non-intervention controls:g = 0.76 95% CI = 0.37, 1.14, relative to active controls (i.e., escitalopram/inert psilocybin; Carhart-Harris et al.2021), which demonstrated a non-significant incremental effect:g = 0.21; 95% CI = −0.31, 0.73. Because Marschall et al. (2022) examined microdosing, we conducted a sensitivity analysis with their study removed. However, omnibus results did not measurably changeg = 0.76; 95% CI = 0.39, 1.14.

Observations

There were a relatively high number of barriers to conducting the current meta-analysis. First, few of the relevant studies comprehensively provided needed statistical information in original texts. This is problematic as clinicians and researchers need such information when trying to appraise this body of work. Underreporting in original texts is somewhat understandable, as comprehensive results must often be relegated to supplementary files (the current paper serves as a prime example). However, many of the supplementary files reviewed lacked necessary information such as depression outcome means at specific time points and deviation estimates. Many authors reported pieces of information in the form of charts, but not exact estimates, and some failed to provide necessary information (e.g., exclusion of Grob et al.2011, see Supplementary File1). In many cases, enough results are provided such that analyses were possible, but hints towards unreported data were evident. For instance, Back et al. (2024), collected between-group measurements at days “1, 8, 15, and 28” (p. 4). However, they only reported means and standard deviations at baseline and day 28. We attempted to contact many authors to obtain such information, but received few responses, with only a subset of authors providing further data. Supplementary File15 provides a record of our attempts to contact corresponding authors of the evaluated RCTs. Four teams failed to respond to multiple inquiries, and two responded after multiple prompts. Our efforts to contact authors included attempts to clarify important pieces of information. For example, we observed in the pre-registration for von Rotz et al. (2023; ClinicalTrials.govNCT03715127) that they initially planned to report depression outcomes at Day 32, yet their publication only presents results up until day 14. There is no documented explanation for this discrepancy. We recommend all psilocybin researchers report means, deviation estimates, andn’s for participants in each treatment and control group for all time points in their studies, in addition to other useful statistical information (95% confidence intervals for respective point estimates,p-values).

Almost all of the RCTs had authors who reported financial conflicts of interest with companies sponsoring psilocybin intervention. We are concerned that such financial conflicts of interest may be adding an additional source of bias. Indeed, this is a well-established corollary of misleading findings that favor respective target pharmacologic agents (Bekelman et al.2003; Ioannidis2005; Mahase2024; Nejstgaard et al.2020). It would be preferable for analyses to be conducted and reported by researchers who are not funded by private companies interested in psychedelic therapies.

Limitations

Our study presents a critical evaluation of the psilocybin-for-depression research program. We offer this synthesis of the research in the spirit of improving psychedelic research. Our study is limited by the relatively small literature body. We observed many interesting trends that could be important moderators. For instance, the microdosing study by Marschall and colleagues (2022) failed to yield a significant incremental effect, but the absence of additional microdosing RCTs prevented us from conducting meaningful meta-regression analyses to evaluate this finding. We also observed that most of the RCTs predominantly included White participants. Presently, psychedelic science is grappling with multicultural, diversity, and social justice issues. Indeed, in contemporary psychedelic science, participants have been relatively demographically homogeneous (Michaels et al.2018). This limits knowledge, makes the external validity of research findings uncertain (Ortiz et al.2022), and stunts our understanding of the ethnopsychopharmacology of psychedelics and psychedelic-assisted therapies (Fogg et al.2021). These inequities are exacerbated by the well-documented need for novel treatments in diverse communities, the rich history of psychedelic use in non-White, indigenous cultures, and the unethical practices that harmed vulnerable groups during prior waves of psychedelic research (Strauss et al.2022). Future researchers should make greater efforts to include diverse participants in their RCTs.

We were unable to disentangle the role of psychotherapeutic facilitation in the intervention. This was one of many issues that recently precluded favorable ratings from the FDA for MDMA PTSD interventions. Some degree of facilitation was apparent in almost every reviewed study, with multiple studies acknowledging concurrent psychotherapy. Ostensibly, this issue should be controlled by the RCT methodologies. However, that is assuming all participants received equal degrees of psychotherapy quality (in terms of alliance, trust, experience, etc.) in each condition per study. As such, it is difficult to ascertain what effects are attributable to psychotherapy and which are attributable to psilocybin, as different therapists might have different skills and levels of patient alliance (that in turn differs by patient). This issue is exacerbated by the noted blinding problem, with therapists being de-blinded by participant reactions, thereby altering their therapeutic approaches between conditions. As noted by Murphy et al. (2022), therapist alliance plays a significant role in psilocybin’s therapeutic effectiveness.

Ethical approval

This study did not involve living subjects and therefore was exempt from ethical board review

Consent to participate

No consent to participate was administered as there were no living subjects.

Consent to publication

All authors consent to publication.

Competing interest

All authors declare no competing interests.

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Supplementary Materials

Data Availability Statement

CMA data sheets are available on the Open Science Framework https://osf.io/p3fcx/.

Data is available from the first author upon request.