Does elimination of placebo responders in double-blind placebo-controlled randomized clinical trials of SSRI antidepressants in depression influence the size of the treatment effect?, a meta-analytic evaluation
Bibliographic record
Abstract
Randomized clinical trials are accepted as the ost effective method to assess the safety and effectiveness of a new drug or clinical intervention. Often, the results of a randomized clinical trial will allow a new drug to be introduced into current clinical practice. Clinical investigators and the pharmaceutical industry naturally want to optimize treatment effect. Thus, many randomized clinical trials, especially those involving psychotropic drugs, are preceded by a "placebo run-in phase", in which all subjects are given the placebo and any subject who responds to the placebo are withdrawn from the study prior to randomization. The objective of this research is to compare the effect size of randomized controlled placebo clinical trials (in the treatment of depression) that include a placebo run-in phase with those that do not include a placebo run-in phase, using a meta-analytic approach. It is hypothesized that the size of the treatment effect will be larger in studies that eliminate placebo responders from the study after a placebo run-in phase. A literature search was carried out to find all available published randomized clinical trials involving the use of a selective serotonin reuptake inhibitor antidepressant and placebo. Data were extracted from the trials and statistical analysis was completed using the international Cochrane Collaboration Review Manager software. The results indicate that there is no statistically significant difference in effect size between the clinical trials that have a placebo run-in phase followed by withdrawal of placebo responders and those trials that do not have such a phase. Recommendations for future research are discussed.
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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 | 0.294 | 0.458 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.033 | 0.068 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".