OP48 A Contextual Model For Evaluating The Value Of Multi-Indication Drugs
Bibliographic record
Abstract
Introduction: An increasing number of anti-cancer medications are indicated for multiple tumors. Existing pharmacoeconomic evaluations routinely examine the cost-effectiveness (CE) and budget impact (BI) of such drugs by indication, as and when each indication is reviewed. The impact of indication-specific conclusions on the holistic value of such medications across all indicated patients is not currently evaluated, yet is important to stakeholders including health technology assessment (HTA) agencies, payers and patients. We introduce a holistic framework that considers the value of multiple indications together at a product level. Application of this approach is illustrated via an example across multiple indications for a novel, targeted anti-cancer therapy (pembrolizumab) in Canada. Methods: Previously-HTA-evaluated indication-specific CE and BI models serve as the foundation for this multi-indication model. Comparing to standard of care (SoC) per indication, the model evaluates the potential BI, clinical outcomes and CE of pembrolizumab among the individual indications along with the overall multi-indication patient population from the perspective of a third-party payer. For the contextual model, incremental costs and quality-adjusted life years (QALYs) were weighted using indication populations derived from national incidence rates. Results: The indication-specific incremental cost-effectiveness ratios (ICER) from CE analyses of ipilimumab-treated advanced melanoma, ipilimumab-naïve advanced melanoma, second-line non-small cell lung cancer (NSCLC), first-line NSCLC and fourth-line classical Hodgkin lymphoma range from USD 52 K to USD 163 K per QALY. Accounting for the relative contributions of the various sizes of indication-specific patient populations results in an overall ICER for pembrolizumab vs. SoC of USD 100 K. Conclusions: A holistic model can provide stakeholders with a tool to evaluate the overall value of multi-indication drugs. Results enable an understanding of the outcomes and economic consequences of treatment with pembrolizumab versus SoC by both individual indications and across all indications. Insights from this contextual approach will enable data from less-developed clinical trials to be considered when previously they might have gone unevaluated by decision-makers.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".