Indirect Treatment Comparisons in Healthcare Decision Making: A Targeted Review of Regulatory Approval, Reimbursement, and Pricing Recommendations Globally for Oncology Drugs in 2021–2023
Bibliographic record
Abstract
INTRODUCTION: Indirect treatment comparisons (ITCs) evaluate novel treatments compared to appropriate comparators when direct evidence is unavailable or infeasible. The objective of this study was to highlight the prevalence of different ITC methods in oncology drug submissions and to provide insights into how ITCs have been used in recent regulatory approval, reimbursement recommendations, or pricing decisions across various regions and diverse assessment frameworks. METHODS: A targeted literature review was conducted to identify assessment documents for oncology drug submissions that included ITCs. This included hand searches of the websites of four regulatory bodies and four health technology assessment (HTA) agencies with varying assessment frameworks across North America, Europe, and Asia-Pacific. RESULTS: A total of 185 documents were included for synthesis. Documents were retrieved from all four HTA agencies and the European Medicines Agency (EMA), the only regulatory body with eligible records. Within these, 188 unique submissions included a total of 306 supporting ITCs of various methods. Authorities more frequently favored anchored or population-adjusted ITC techniques for their effectiveness in data adjustment and bias mitigation. Furthermore, ITCs in orphan drug submissions more frequently led to positive decisions compared to non-orphan submissions. CONCLUSIONS: This review highlights the crucial role and widespread use of ITCs in global healthcare decision-making, particularly when direct evidence is lacking, and in the discernment of market-specific clinical benefits. This work contributes to bolstering the credibility and recognition of ITCs across regulatory and HTA agencies of diverse regions and assessment frameworks.
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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 | 0.209 | 0.475 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.018 | 0.018 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".