Meta-Analysis of Placebo Rates in Major Depressive Disorder Trials
Bibliographic record
Abstract
BACKGROUND: Placebo effects in major depressive disorder (MDD) have received much interest in the medical literature. However, few quantitative analyses have been done in homogeneous populations. OBJECTIVE: To determine efficacy rates for placebo in patients with MDD; to quantify the correlation between efficacy and publication year, as well as between placebo and drug response rates. DESIGN: Searching MEDLINE (1966-December 2000), EMBASE (1998-February 2001), HealthSTAR (1975-December 2000), and Cochrane (1980-December 2000) databases, randomized, placebo-controlled trials were retrieved including patients with MDD as defined by Diagnostic and Statistical Manual of Mental Disorders, 3rd and 4th editions criteria, Hamilton Rating Scale for Depression score >/=18 or Montgomery-Asberg Depression Rating Scale score >/=16, reporting successes as 50% decreases in scores after 6-8 weeks of treatment. Response rates were summarized using a random effects meta-analysis for per protocol (PP) and intent-to-treat (ITT) results. RESULTS: We included 24 of 134 potential studies examining 4459 patients, 1786 on placebo and 2673 on an antidepressant. Placebo response rates were 45.5% (PP) and 26.9% (ITT). Correlations were significant between year and rates (PP rho 0.448, p = 0.042; ITT rho 0.557; p = 0.006), but not for active drugs. Placebo and drug rates were correlated (PP r 0.397, p = 0.020; ITT r 0.539; p = 0.002). CONCLUSIONS: These placebo rates confirm those reported previously, but were from a homogeneous population. Although statistically significant, the correlation between drug and placebo rates was lower than others reported. During the study period, placebo rates increased linearly; active drugs did not. Correlations between placebo and drug response rates reflected moderate to strong effect sizes. We suggest that current methodology has been unsuccessful in achieving unbiased double-blind conditions not influenced by extra-trial factors, including time.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.009 | 0.006 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".