Influence of statistician involvement on reporting of randomized clinical trials in medical oncology
Bibliographic record
Abstract
Ideally, statisticians should be involved in the design, analysis, and reporting of randomized clinical trials (RCTs). This study assessed the impact of a statistician involvement in published medical oncology RCTs between 2005 and 2009. The reporting quality of each publication was rated using the Overall Reporting Quality Score on the basis of either 2001 or 2010 Consolidated Standards of Reporting Trials criteria. A four-question email survey on the statistical design and analysis was sent to the corresponding authors of each trial. Nonresponders were approached a maximum of three times. Overall, 107 responses were received from 357 solicited authors (30%). Corresponding authors from industry-funded RCTs were less likely to respond (51 vs. 65%, P=0.013). The same person was responsible for statistical design and analyses in 47% of cases. Overall, the statistician involved held a PhD (or equivalent) in statistics in most cases. The statisticians responsible for the statistical design and analysis were listed as coauthors in 68 and 81% of RCT manuscripts. There was no statistically significant impact on manuscript reporting quality of the degree of statistician involvement in manuscript preparation. Fewer trials were reported as positive when the responsible statistician was listed as a coauthor. It is possible that RCTs included in this review are in general of higher quality or were more likely to have a greater level of statistician involvement than smaller, single-arm, or unpublished studies. This imbalance could explain the lack of significant difference observed in the Overall Reporting Quality Score between trials where statisticians were listed as coauthors or not.
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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 | 0.839 | 0.946 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.010 | 0.015 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".