Does Inclusion of a Placebo Arm Influence Response to Active Antidepressant Treatment in Randomized Controlled Trials?
Bibliographic record
Abstract
OBJECTIVE: To determine if the inclusion of a placebo arm and/or the number of active comparators in antidepressant trials influences the response rates of the active medication and/or placebo. DATA SOURCES: Searches of MEDLINE, PsycINFO, and pharmaceutical Web sites for published trials or trials conducted but unpublished between January 1996 and October 2007. STUDY SELECTION: 2,275 citations were reviewed, 285 studies were retrieved, and 90 were included in the analysis. Trials reporting response and/or remission rates in adult subjects treated with an antidepressant monotherapy for unipolar major depression were included. DATA EXTRACTION: The primary investigator recorded the number of responders and/or remitters in the intent-to-treat population of each study arm or computed these numbers using the quoted rates. DATA SYNTHESIS: Poisson regression analyses demonstrated that mean response rate for the active medication was higher in studies comparing 2 or more active medications without a placebo arm than in studies comparing 2 or more active medications with a placebo arm (65.4% vs 57.7%, P < .0001) or in studies comparing only 1 active medication with placebo (65.4% vs 51.7%, P = .0005). Mean response rate for placebo was significantly lower in studies comparing 1 rather than 2 or more active medications (34.3% vs 44.6%, P = .003). Mean remission rates followed a similar pattern. Meta-analysis confirmed results from the pooled analysis. CONCLUSIONS: These data suggest that antidepressant response rates in randomized control trials may be influenced by the presence of a placebo arm and by the number of treatment arms and that placebo response rates may be influenced by the number of active treatment arms in a study.
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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 | 0.344 | 0.663 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.022 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".