Assessing the ‘true’ effect of active antidepressant therapy <i>v.</i> placebo in major depressive disorder: use of a mixture model
Bibliographic record
Abstract
BACKGROUND: There is controversy about the implications of relatively small average drug-placebo differences observed in randomised controlled trials of antidepressant medications. AIMS: To investigate whether efficacy is better understood as a large effect in a subgroup of patients. METHOD: The mixture model was used to identify patient subgroups (patients benefiting or not benefiting from treatment) to directly model the skewness of Montgomery-Åsberg Depression Rating Scale (MADRS) scores at week 8. RESULTS: The MADRS scores improved by 15.9 points (95% CI 15.2-16.6) among patients who benefited from treatment. The proportion of patients who benefited from escitalopram and not from placebo treatment was 19.5%, corresponding to a number needed to treat of 5. CONCLUSIONS: This model gave a considerably better fit to the data than the analysis of covariance model in which all patients were assumed to benefit from treatment. The small average antidepressant-placebo difference obscures a much larger effect in a clinically meaningful subgroup of patients.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".