Toward Best Practices for Economic Evaluations of Tumor-Agnostic Therapies: A Review of Current Barriers and Solutions
Bibliographic record
Abstract
OBJECTIVES: Cancer therapies targeting tumor-agnostic biomarkers are challenging traditional health technology assessment (HTA) frameworks. The high prevalence of nonrandomized single-arm trials, heterogeneity, and small benefiting populations are driving outcomes uncertainty, challenging healthcare decision making. We conducted a structured literature review to identify barriers and prioritize solutions to generating economic evidence for tumor-agnostic therapies. METHODS: We searched MEDLINE and Embase for English-language studies conducting economic evaluations of tumor-agnostic treatments or exploring related challenges and solutions. We included studies published by December 2022 and supplemented our review with Canadian Agency for Drugs and Technologies in Health and National Institute for Health and Care Excellence technical reports for approved tumor-agnostic therapies. Three reviewers abstracted and summarized key methodological and empirical study characteristics. Challenges and solutions were identified through authors' statements and categorized using directed content analysis. RESULTS: Twenty-six studies met our inclusion criteria. Studies spanned economic evaluations (n = 5), reimbursement reviews (n = 4), qualitative research (n = 1), methods validations (n = 3), and commentaries or literature reviews (n = 13). Challenges encountered related to (1) the treatment setting and clinical trial designs, (2) a lack of data or low-quality data on clinical and cost parameters, and (3) an inability to produce evidence that meets HTA guidelines. Although attempted solutions centered on analytic approaches for managing missing data, proposed solutions highlighted the need for real-world evidence combined with life-cycle HTA to reduce future evidentiary uncertainty. CONCLUSIONS: Therapeutic innovation outpaces HTA evidence generation and the methods that support it. Existing HTA frameworks must be adapted for tumor-agnostic treatments to support future economic evaluations enabling timely patient access.
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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 | 0.494 | 0.737 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.012 | 0.014 |
| Bibliometrics | 0.051 | 0.044 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.026 | 0.026 |
| Open science | 0.012 | 0.010 |
| Research integrity | 0.012 | 0.016 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".