Does Outcome Reporting Bias “Cause” Cancer? Risks Associated with Hidden Data on Angiotensin Receptor Blockers
Bibliographic record
Abstract
Conflicting reports have been published regarding the influence of angiotensin receptor blockers (ARBs) on the incidence of cancer. One meta-analysis reported a 1% absolute increase in the incidence of cancer associated with ARBs over 4 years. Contrasting findings were reported in an industry-sponsored meta-analysis and in another meta-analysis, both of which showed no difference in the incidence of cancer in ARB treatment groups relative to control groups. The US Food and Drug Administration has recently asserted that evidence does not support an association between ARBs and the development of cancer. The current review compares the 3 published meta-analyses assessing the association between ARBs and cancer and shows that outcome reporting bias contributed to the conflicting results. Given the prevalence of this form of bias in the scientific literature, the processes for systematic reviews and meta-analyses are under siege, and there is an important role for health care regulators to play. If all outcome data from clinical trials were to be reported in the public domain, independent analyses could be performed and the results of industry-sponsored trials verified. Furthermore, if regulators were to mandate the publication, in the public domain, of all clinical outcomes collected in clinical trials, outcome reporting bias could be eliminated.RÉSUMÉContexte : Des rapports contradictoires ont été publiés quant à l’influence des antagonistes des récepteurs de l’angiotensine (ARA) sur l’incidence du cancer. Une méta-analyse a signalé une augmentation absolue de 1 % de l’incidence du cancer associée aux ARA sur une période de quatre ans. Des résultats très différents issus de deux méta-analyses, dont l’une a été commanditée par l’industrie, n’ont montré aucune différence dans l’incidence du cancer dans les groupes traités par les ARA comparativement aux groupes témoins. La Food and Drug Administration des États-Unis a déjà affirmé que les données probantes ne corroborent pas le lien entre les ARA et l’apparition de cancer. La présente analyse compare les trois méta-analyses publiées évaluant le lien entre les ARA et le cancer, et montre que les biais de publication des résultats ont contribué à ces conclusions contradictoires. Étant donné la prévalence de cette forme de biais dans la littérature scientifique, la démarche entourant les analyses systématiques et les méta-analyses est sur la sellette et les organismes de réglementation des soins de santé ont un important rôle à jouer dans ce contexte. Si toutes les données issues des études cliniques étaient rendues publiques, des analyses indépendantes pourraient être effectuées et les résultats des études commanditées par l’industrie pourraient être vérifiés. En outre, si les organismes de réglementation exigeaient de rendre publiques l’ensemble des données issues des études cliniques, on pourrait alors éliminer les biais de publication des résultats.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; a candidate call from one teacher head, not a consensus.
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".