Is it time to shift to better characterization of patients in trials assessing novel antidepressants? An example of two relapse prevention studies with agomelatine
Bibliographic record
Abstract
The present paper reports in parallel the findings of the two studies that evaluated the efficacy of agomelatine in preventing relapse of depression. It describes the methodological adjustments made between the first and the second trial, particularly in relation to patient selection and accuracy of diagnosis of depression. Patients with major depressive disorder who responded to an 8/10-week course of agomelatine 25-50 mg treatment were randomly assigned to receive continuation treatment with agomelatine or placebo during a 24-week, randomized, double-blind treatment period with an optional 18- or 20-week double-blind extension period. The cumulative probability of relapse was calculated using the Kaplan-Meier method of survival analysis. Study 1 lacked assay sensitivity because of an unexpectedly low relapse rate in the placebo arm, but was instructive in showing that the agomelatine effect was better than placebo only in those patients with higher symptom levels at baseline. Study 2 showed a robust benefit of agomelatine - a two-fold reduction in the relapse rate - observed at least up to 10 months in both the overall population and the more severely depressed patients. The methodological adjustments introduced in study 2 (e.g. a minimum subscore calculated from eight specific Hamilton Depression Rating Scale items, the use of the self-rating questionnaire Hospital Anxiety Depression Scale and the Sheehan questionnaire) have assured an adequate severity of depression not only on the basis of ratings of symptom severity but also on measures of functional impairment. We did not find increased severity of symptoms in study 2, but we hypothesize that the increased demands on investigators improved the quality of recruitment to represent more real-world patients. Adopting these innovations could contribute towards lower failure rates for future placebo-controlled clinical trials in the field.
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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 | 0.346 | 0.333 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.007 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.009 | 0.015 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.015 | 0.020 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".