A preliminary application of a haemophilia value framework to emerging therapies in haemophilia
Bibliographic record
Abstract
INTRODUCTION: Emergence of new therapies are anticipated to improve clinical outcomes and quality of life of persons with haemophilia. Challenges in conducting randomized clinical trials in rare diseases have resulted in a lack of direct head-to-head comparisons to support value-based decision-making between different treatments. METHODS: We conducted a literature review for new and emerging haemophilia A and B therapies (extended half-life [EHL] replacement factor, non-replacement therapies [NRT], and gene therapies [GT]) to identify differentiating patient-centred outcomes defined previously in a haemophilia value framework. Since the literature included all publication types (e.g., surveys, modelling studies, commentaries/reviews), collected data were assigned level of evidence scores. RESULTS: Across different classes of therapies, bleeding was determined as the most frequently reported differentiating outcome, with EHL, NRT, and GT each demonstrating an advantage over comparator replacement therapies. EHL therapies for haemophilia A and B and NRT for haemophilia A showed good representation across Tier 1 outcomes (health status achieved/retained), while more publications were identified with Tier 2 (process of recovery) outcomes for NRT than EHL or GT. In Tier 3 (sustainability of health), frequency of breakthrough bleeds represented a differentiating outcome for EHL (both haemophilia A and B), NRT (haemophilia A only), and GT (haemophilia B only), whereas sustained good health was differentiating for most comparisons. CONCLUSIONS: We demonstrate the utility of the haemophilia value framework as a common core outcome set for effectively comparing therapies. Application of this framework will serve as a useful decision-making tool for patients, clinicians, and within health technology assessments. KEY POINTS OF CONSIDERATION: With the emergence of high-cost, paradigm changing treatments across multiple areas of medicine, we, the haemophilia community, need to be equipped to meet the growing demands for more rigorous evidence-based value assessments using the tools expected by assessors. The traditional access toolbox needs to evolve to meet the paradigm shift in treatment options. Value can no longer be defined by annualized bleed rates alone. To realize the full impact of new therapies, we need to utilize tools, such as a value framework, to organize evidence, identify data gaps, and assess patient-defined, meaningful outcomes across a multi-faceted dimension. The haemophilia value framework is an effective tool for organizing the available evidence and identifying gaps in the evidence. This can be used for assessing the value of emerging therapies in haemophilia utilizing data generated through randomized clinical trials and real world evidence generation. This is a call for incorporating the Value Framework into official submissions to authorities, as it captures a broader range of outcomes, including patient meaningful outcomes, in ways that better assess the potential benefits of new therapies.
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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 | 0.047 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".