Strategic use of new generation antidepressants for depression: SUN(^_^) D protocol update and statistical analysis plan
Bibliographic record
Abstract
BACKGROUND: SUN(^_^)D, the Strategic Use of New generation antidepressants for Depression, is an assessor-blinded, parallel-group, multicenter pragmatic mega-trial to examine the optimum treatment strategy for the first- and second-line treatments for unipolar major depressive episodes. The trial has three steps and two randomizations. Step I randomization compares the minimum and the maximum dosing strategy for the first-line antidepressant. Step II randomization compares the continuation, augmentation or switching strategy for the second-line antidepressant treatment. Step III is a naturalistic continuation phase. The original protocol was published in 2011, and we hereby report its updated protocol including the statistical analysis plan. RESULTS: We implemented two important changes to the original protocol. One is about the required sample size, reflecting the smaller number of dropouts than had been expected. Another is in the organization of the primary and secondary outcomes in order to make the report of the main trial results as pertinent and interpretable as possible for clinical practices. Due to the complexity of the trial, we plan to report the main results in two separate reports, and this updated protocol and the statistical analysis plan have laid out respective primary and secondary outcomes and their analyses. We will convene the blind interpretation committee before the randomization code is broken. CONCLUSION: This paper presents the updated protocol and the detailed statistical analysis plan for the SUN(^_^)D trial in order to avoid reporting bias and data-driven results. TRIAL REGISTRATION: ClinicalTrials.gov: NCT01109693 (registered on 21 April 2010).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".