Financial Conflicts of Interest in Propensity Score-Matched Studies Evaluating Biologics and Biosimilars for Inflammatory Bowel Disease
Bibliographic record
Abstract
Abstract Background Propensity score matching (PSM), a statistical technique that estimates a treatment effect by accounting for predictor covariates, has been used to evaluate biologics for inflammatory bowel disease (IBD). Financial conflicts of interest are prevalent in the marketing of biologic medications. It is unclear whether this burden of conflicts is present among authors of PSM studies comparing IBD biologics and biosimilars. Objective This study was aimed to determine the prevalence of financial conflicts of interest among authors of PSM studies evaluating IBD biologics and biosimilars. Methods We conducted a systematic search for PSM studies comparing biologics and biosimilars in IBD treatment. We identified 21 eligible studies. Two independent authors extracted self-declared conflicts from the disclosures section. Each participating author was searched on the Centers for Medicare & Medicaid Services Open Payments to identify payment amounts and undisclosed conflicts. Primary outcome was the prevalence of author conflicts. Secondary analyses assessed for an association between conflict prevalence and reporting of positive outcomes. Results Among 283 authors, conflicts were present among 41.0% (116 of 283). Twenty-three per cent (27 of 116) of author conflicts involved undisclosed payments. Studies with positive outcomes were significantly more likely to include conflicted authors than neutral studies (relative risk = 2.34, 95% confidence interval: 1.71 to 3.21, P < 0.001). Conclusions Overall, we found a high burden of undisclosed conflicts among authors of PSM studies comparing IBD biologics and biosimilars. Given the importance of PSM studies as a means for biologic comparison and the potential for undue industry influence from these payments, authors should ensure greater transparency with reporting of industry relationships.
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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 | 0.077 | 0.281 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.014 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".