Does access to clinical study reports from the European Medicines Agency reduce reporting biases? A systematic review and meta-analysis of randomized controlled trials on the effect of erythropoiesis-stimulating agents in cancer patients
Bibliographic record
Abstract
Since 2010, the European Medicines Agency (EMA) has provided access to clinical study reports (CSRs). We requested CSRs for randomized controlled trials (RCTs) of erythropoiesis-stimulating agents (ESAs) in cancer patients from EMA and identified RCT publications with literature searches. We assessed CSR availability and completeness, the impact of unreported and unpublished data obtained from CSRs on the effects of ESAs on quality of life (QoL) of cancer patients, and discrepancies between data reported in the public domain and in CSRs. We used random-effects meta-analyses to evaluate the effect of ESAs on QoL measured with Functional Assessment of Cancer Therapy-Anemia (FACT-An), FACT-Fatigue (FACT-F) and FACT-Anemia Total (FACT-An Total) stratified by data source and the impact of discrepancies on QoL, mortality, adverse events, and clinical effectiveness outcomes. We identified 94 eligible RCTs; CSRs or other study documentation were available for 17 (18%) RCTs at EMA. Median report length was 1,825 pages (range 72-14,569). Of 180 outcomes of interest reported in the EMA documentation, 127 (71%) were publicly available. For 80 of those (63%) we noted discrepancies, but these had little impact on the pooled effect estimates. Of 27 QoL outcomes reported in the CSRs, 17 (63%) were unpublished. Including six unpublished comparisons (pooled mean difference [MD] 0.20; 95% confidence interval [CI] -1.93, 2.33) reduced the pooled effect of ESAs for FACT-An from MD 5.51 (95% CI 4.20, 6.82) in published data to MD 3.21 (95% CI 1.38, 5.03), which is below a clinically important difference (defined as MD ≥4). Effects were similar for FACT-F and FACT-An Total. Access to CSRs from EMA reduced reporting biases for QoL outcomes. However, EMA received documentation for a fraction of all RCTs on effects of ESAs in cancer patients. Additional efforts by other agencies and institutions are needed to make CSRs universally available for all RCTs.
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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 | 0.393 | 0.750 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.017 | 0.025 |
| Bibliometrics | 0.022 | 0.024 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.007 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".