Baseline severity and the prediction of placebo response in clinical trials for alcohol dependence: A meta‐regression analysis to develop an enrichment strategy
Bibliographic record
Abstract
BACKGROUND: There is considerable unexplained variability in alcohol abstinence rates (AR) in the placebo groups of randomized controlled trials (RCTs) for alcohol dependence (AD). This is of particular interest because placebo responses correlate negatively with treatment effect size. Recent evidence suggests that the placebo response is lower in very heavy drinkers who show no "spontaneous improvement" prior to treatment initiation (high-severity population) than in a mild-severity population and in studies with longer treatment duration. We systematically investigated the relationship between population severity, treatment duration, and the placebo response in AR to inform a strategy aimed at reducing the placebo response and thereby increasing assay sensitivity in RCTs for AD. METHODS: We conducted a systematic literature review on placebo-controlled RCTs for AD.We assigned retained RCTs to high- or mild-severity groups of studies based on baseline drinking risk levels and abstinence duration before treatment initiation. We tested the effects of population severity and treatment duration on the placebo response in AR using meta-regression analysis. RESULTS: Among the 19 retained RCTs (comprising 1996 placebo-treated patients), 11 trials were high-severity and 8 were mild-severity RCTs. The between-study variability in AR was lower in the high-severity than in the mild-severity studies (interquartile range: 7.4% vs. 20.9%). The AR in placebo groups was dependent on population severity (p = 0.004) and treatment duration (p = 0.017) and was lower in the high-severity studies (16.8% at 3 months) than the mild-severity studies (36.7% at 3 months). CONCLUSIONS: Pharmacological RCTs for AD should select high-severity patients to decrease the magnitude and variability in the placebo effect and and improve the efficiency of drug development efforts for AD.
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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 | 0.257 | 0.113 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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; both teacher heads 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".