Does elimination of placebo responders in a placebo run-in increase the treatment effect in randomized clinical trials? A meta-analytic evaluation
Bibliographic record
Abstract
The use of a placebo run-in phase, in which placebo responders are withdrawn from a study before random assignment to treatment condition, has been criticized as favoring the active treatment in clinical trials. We compared the effect size of randomized, placebo-controlled clinical trials (in the treatment of depression with selective serotonin reuptake inhibitors [SSRIs]) that include a placebo run-in phase with those that do not, using a meta-analytic approach. This study differed from earlier meta-analytic studies in that it considered only SSRIs and included only studies using continuous measures of depression, allowing for a more refined assessment of effect size. An extensive literature search identified 43 datasets published between 1980 and 2000 comparing placebo with SSRI and using a continuous measure of depression (usually the Hamilton Depression Rating Scale). We included only studies of at least 6 weeks' duration focusing on treatment for primary acute major depression in adults 18-65 years of age. Studies focusing on depression in specific medical illnesses were not included. Analysis of efficacy was based on 3047 subjects treated with an SSRI antidepressant and 3740 subjects treated with a placebo. There was no statistically significant difference in effect size between the clinical trials that had a placebo run-in phase followed by withdrawal of placebo responders and those trials that did not. Despite the lack of a statistically significant difference between studies of withdrawing early placebo responders and those not using this procedure, this approach is likely to continue to be used widely because it produces large absolute effect sizes. It is recommended that future studies clearly describe these procedures and report the number of subjects dropped from the study for early placebo response and other reasons.
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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 | 0.357 | 0.486 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.036 | 0.062 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.009 | 0.007 |
| 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".