Meta-Regression Analysis of Placebo Response in Antipsychotic Trials, 1970–2010
Bibliographic record
Abstract
OBJECTIVE: Large placebo response presents a major challenge for psychopharmacologic drug development and contributes to the increasing failure of psychiatric trials. The objective of this meta-regression analysis was to identify potential contributors to placebo response in randomized controlled trials of antipsychotic treatment in schizophrenia. METHOD: The authors extracted trial design and clinical variables from eligible randomized controlled trials (N=50) identified through searches of MEDLINE (1960-2010) and other sources. Standardized mean change (SMC) was used as the effect size measure for placebo response, based on change scores on the Brief Psychiatric Rating Scale or the Positive and Negative Syndrome Scale from baseline to endpoint (2 to 12 weeks). RESULTS: The results suggest significant heterogeneities (Q=387.83, df=49) in the magnitude of placebo response (mean SMC, -0.33, range -1.4 to 0.9) and in study quality. Both placebo SMC and study quality increased over time. Younger age, shorter duration of illness, greater baseline symptom severity, and shorter trial duration were significantly associated with greater placebo response, while country (United States compared with other countries) was not. More study sites, fewer university or Veterans Affairs treatment settings, and a lower percentage of patients assigned to receive placebo were associated with a greater placebo response, but these were not independent of publication year. Study quality affected the variability but not mean levels of placebo response. CONCLUSIONS: This study identified important patient characteristics and trial design factors affecting the level of placebo response and hence the likelihood of detecting efficacy signals in randomized controlled trials. Future studies should test whether controlling these factors improves the detection of an antipsychotic effect.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.082 | 0.144 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.068 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".