A scoping review on two-stage randomized preference trial in the field of mental health and addiction
Bibliographic record
Abstract
Abstract Background Randomized Controlled Trial is the most rigorous study design to test the efficacy and effectiveness of an intervention. Patient preference may negatively affect patient performance and decrease the generalizability of a trial to clinical population. Patient preference trial have particular implications in the field of mental health and addiction since mental health interventions are generally complex, blinding of intervention is often difficult or impossible, patients may have strong preference, and outcome measures are often subjective patient self-report which may be greatly influenced if patient’s preference did not match with the intervention received. Methods In this review, we have surveyed the application of two-stage randomized preference trial with focus on studies in the field of mental health and addiction. The study selection followed the guideline provided by Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews. Results Six two-stage randomized preference trials (ten publications) have been identified in the field of mental health field and addiction. In these trials, the pooled dropout rates were 18.3% for the preference arm, and 28.7% for the random arm, with a pooled RR of 0.70 (95% CI, 0.56–0.88; P = 0.010) indicating lower risk of dropout in the preference arm. The standardized preference effects varied widely from 0.07 to 0.57, and could be as large as the treatment effect in some of the trials. Conclusion This scoping review has shown that two-stage randomized preference trials are not as popular as expected in mental health research. The results indicated that two-stage randomized preference trials in mental health would be beneficial in retaining patients to expand the generalizability of the trial.
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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 | 0.157 | 0.369 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.016 |
| Bibliometrics | 0.011 | 0.014 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.007 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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; a candidate call from one source (direct Gemma or distilled Codex), 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".