Completeness of reporting of simulation studies on responder analysis methods and simulation performance: a methodological survey
Bibliographic record
Abstract
OBJECTIVES: To evaluate the completeness of reporting of simulation studies on responder analysis methods and simulation performance. DESIGN: Systematic methodological survey. DATA SOURCES: We searched Embase, MEDLINE (via Ovid), PubMed and Web of Science Core Collection from inception to 9 October 2023. ELIGIBILITY CRITERIA: We included simulation studies comparing responder analysis methods and assessing simulation performance (bias, accuracy, precision or variance, power, type I and II errors and coverage). DATA EXTRACTION AND SYNTHESIS: Two independent reviewers extracted data and assessed simulation performance. We used descriptive analyses to summarise reporting quality and simulation performance. RESULTS: We identified seven simulation studies exploring augmented binary methods, distributional methods and model-based methods. No studies reported the starting seed, occurrence of failures during simulations, the random number generator used and the number of simulations. No studies reported simulation accuracy. Responder analysis results were not significantly influenced by covariate adjustment. Distributional methods remained adaptable even with skewed data. Compared with standard binary methods, augmented binary methods generated increased power and precision. When the threshold is in the tail of the distribution, a simple asymptotic Bayesian (SAB) distributional approach may not reduce uncertainty but can improve precision. CONCLUSION: Simulation studies comparing responder analysis methods exhibit suboptimal reporting quality. Compared with standard binary methods, augmented binary methods, distributional methods and model-based methods may be better choices, but there is no best one.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Reporting · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Metaresearch Domain: Reporting · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.631 | 0.889 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.010 | 0.014 |
| Bibliometrics | 0.028 | 0.028 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".