Are cancer cost-effectiveness analyses presented at major conferences published timely without bias? A systematic review
Bibliographic record
Abstract
6560 Background: Cancer cost-effectiveness analyses (CEA) should be published timely without publication bias of studies with more favourable incremental cost-effectiveness ratios (ICER) in order to be informative to policy makers and stakeholders. This study examined the publication pattern of CEA presented in the annual meetings of the American Society of Clinical Oncology (ASCO) and the international annual meetings of the International Society of Pharmacoeconomics and Outcome Research (ISPOR). Methods: Abstracts from ASCO and ISPOR from 1997 to 2007 were reviewed. CEA with primary outcomes as incremental cost per life year gained or quality-adjusted life year (QALY) were included. Data including ICER (adjusted to USD), cancer type, country, and quality indicators were extracted. Publication search was conducted using Medline, HealthStar and EconLit. Time-to-publication analysis was conducted with exploratory analyses on predictors of publication. Results: 137 abstracts were included. Only 48 were published. The actuarial 1-, 2-, 3-, 5-year publication rates were 12%, 25%, 33%, 42%, respectively. CEA using a lifetime horizon (HR = 2.3, p = 0.01) were more likely to be published. There were trends of CEA presented in ASCO (HR = 1.8, p = 0.07) or on breast cancer (HR = 1.7 p = 0.06) towards being published. Favourable ICER and country did not predict publication. Among the 77 cost-utility analyses, when using cutoff at $20,000, $50,000, and $100,000/QALY, 55%, 82%, and 87% were cost-effective, respectively. US abstracts were more likely to report ICER > $50,000/QALY (OR = 4, p = 0.05). Quality indicators such as the use of lifetime horizon, societal perceptive, and probabilistic sensitivity analysis were stated explicitly in only 22%, 17%, and 16% of abstracts, respectively. Conclusions: The publication rate of CEA abstracts was low and not timely for open discussions among stakeholders when making policy decision. While there was no direct evidence of publication bias of favourable ICER CEA on the abstract-to-publication level, very favourable ICERs were reported by most abstracts, which might suggest non-submission of unfavourable ICER CEA for abstract presentation. The overall quality of study design or reporting of CEA abstracts appeared suboptimal. No significant financial relationships to disclose.
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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 | 0.166 | 0.599 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.013 | 0.013 |
| Bibliometrics | 0.032 | 0.040 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".