Bias in reporting of endpoints of efficacy and toxicity in randomized clinical trials (RCTs) for women with breast cancer (BC).
Bibliographic record
Abstract
6043 Background: Phase III RCTs are designed to assess clinically important differences in endpoints that reflect benefit to patients. Accurate and unbiased reporting is essential to guide rational therapy. Here we evaluate the quality of reporting of the primary endpoint (PE) and of toxicity in RCTs for BC. Methods: PUBMED was searched from 1995-2011 to identify RCTs for BC. Scales for assessing bias in reporting of the PE and of toxicity were developed. For the PE, scales assessed whether the concluding statement of the abstract (CSAbs) was (a) based on the PE, (b) described appropriately a statistically positive or negative result for the PE, or (c) was based on secondary endpoints. Bias and completeness of reporting of toxicity was assessed using a hierarchy scale of whether reporting occurred in the CSAbs, elsewhere in the abstract, in the discussion of the article or only in the results. Association of bias with Journal Impact Factor (JIF); changes in the PE compared to protocol information in clinicaltrials.gov and funding source was also evaluated. Results: 164 trials were evaluated; 33% showed bias in reporting of the PE. The PE was more likely to be reported in the CSAbs if statistically significant [OR 5.2, 95% CI 1.9-14.3, p=0.001]. A statistically negative PE was not reported in the CSAbs in 27% of trials. Of the 30 trials where protocol information was available in clinicaltrials.gov, there were non-significant associations for the PE to show a positive result if had been changed, and for greater bias in reporting the PE if it was unchanged. 67% of studies showed bias in reporting of toxicity. Only 14% mentioned toxicity in CSAbs. When the PE was positive, bias in reporting of toxicity was more common [OR 2.0, 95% CI 1.0-3.9, p=0.04]. There was no apparent association between bias in reporting of either the PE or toxicity and JIF or funding source, but a non-significant association between change in the PE and industry funding. Conclusions: Bias in reporting of the PE is common especially for studies with a negative PE. Reporting of toxicity is poor especially for studies with a positive PE. Changing of the PE appears to be a strategy to increase the likelihood of observing a statistically significant result.
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 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 | 0.624 | 0.835 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.015 |
| Bibliometrics | 0.014 | 0.017 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".