Evaluating multiplicity reporting in analgesic clinical trials: An analytical review
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Analgesia trials often demands multiple comparisons to assess various treatment arms, outcomes, or repeated assessments. These multiple comparisons risk inflating the false positive rate. Multiplicity correction in recent analgesic randomized controlled trials (RCTs) remains unclear despite statistical method advancements and regulatory guidelines. Our study aimed to identify reporting inadequacies in multiple analysis adjustments and explanations to understand these deficiencies. DATABASES AND DATA TREATMENT: This review analysed RCTs from the European Journal of Pain, the Journal of Pain, and PAIN, published between January 2018 and December 2022. We included randomized, double-blind trials focusing on pain outcomes. Data extraction, managed by three researchers using predefined criteria, included trial characteristics, multiplicity presence, and correction methods. Descriptive statistical analyses included Fisher's exact, and Holm method for multiple comparisons. RESULTS: Out of 112 articles, 48 pre-specified a primary analysis plan. Multiple analyses were observed in 65 articles, with 60% adjusting for all comparisons, primarily using the Bonferroni method. Compared with previous studies, no significant changes in multiplicity correction practices were noted when stratified by trial type, size, and sponsor. CONCLUSIONS: The study reveals a persistent reliance on multiple comparisons in analgesic clinical trials without a corresponding increase in multiplicity corrections emphasizing a need for enhanced reporting and implementation of statistical adjustments. We acknowledge limitations in categorizing studies, the use of a surrogate for the trial stage, and sourcing data from journal webpages rather than a database. SIGNIFICANCE STATEMENT: This study flags inadequate reporting on multiplicity correction in analgesic trials, stressing the risk of false positives and the urgent need for enhanced reporting to boost reproducibility.
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
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 | Systematic review | low |
| gpt | Metaresearch Domain: Reporting · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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 | 0.525 | 0.856 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.010 | 0.016 |
| Bibliometrics | 0.040 | 0.036 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.005 | 0.004 |
| 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.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".