Differences in Incremental Cost-Effectiveness Ratios for Common Versus Rare Conditions: A Case from Oncology
Bibliographic record
Abstract
BACKGROUND: Incremental cost-effectiveness ratios (ICERs) are used to assess the value for money of new drugs. Many believe that ICERs for drugs that treat rare diseases are much higher than those of common drugs. Our objective was to compare the proportion of ICERs that are cost effective for rare and common cancers. METHODS: We used the Tufts Medical Center Cost-Effectiveness Analysis (CEA) Registry to identify cost-effectiveness studies of pharmaceutical interventions for cancers. Studies that assessed FDA-approved 'orphan drugs' were categorized as assessing rare cancers. The proportion of common and rare cancer drugs that were cost effective at various ICER thresholds were compared along with study characteristics. Logistic regressions were conducted to assess important predictors of cost effectiveness. RESULTS: We identified 303 studies that reported 701 ICERs. Seventy nine percent (n = 240) of studies evaluated drugs for common cancers. At a threshold of US$50,000/QALY, 58% (n = 321) of ICERs for drugs treating common cancers and 64% (n = 94) of ICERs for drugs treating rare cancers were cost effective (p = 0.23). At US$100,000/QALY, 74% (n = 409) of ICERs for common cancers and 78% (n = 115) of ICERs for rare cancers were cost effective (p = 0.35). Results from the logistic regressions demonstrated that rarity was not a statistically significant predictor of cost effectiveness at both thresholds with publication year, study sponsorship, and cancer type as covariates. CONCLUSIONS: The proportion of ICERs that were cost effective at both thresholds does not appear to be significantly different between the two groups. Rarity is not statistically significantly associated with cost effectiveness, even when adjusted for important covariates.
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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 | 0.034 | 0.148 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".