Systematic review of cost-effectiveness modelling studies for haemophilia
Bibliographic record
Abstract
AIMS: Haemophilia is a rare genetic disease that hinders blood clotting. We aimed to review model-based cost-effectiveness analyses (CEAs) of haemophilia treatments, describe the sources of clinical evidence used by these CEAs, summarize the reported cost-effectiveness of different treatment strategies, and assess the quality and risk of bias. METHODS: We conducted a systematic literature review of model-based CEAs of haemophilia treatments by searching databases, the Tufts Medical Center CEA registry, and grey literature. We summarized and qualitatively synthesized the approaches and results of the included CEAs, without a meta-analysis due the diversity of the studies. RESULTS: 32 eligible studies were performed in 12 countries and reported 53 pairwise comparisons. Most studies analysed patients with haemophilia A rather than haemophilia B. Comparisons of prophylactic versus on-demand treatment indicated that prophylaxis may not be cost-effective, but there was no clear consensus. Emicizumab was generally cost-effective compared with clotting factor treatments and was always dominant for patients with inhibitors. Immune tolerance induction following a Malmö protocol was found to be cost-effective compared to bypassing agents, while there was no consensus for the other protocols. Gene therapies as well as treatment with extended half-life coagulation factors were always cost-effective over their comparators. Studies were highly heterogenous regarding their time horizons, model structures, the inclusion of bleeding-related mortality and quality-of-life impacts. This heterogeneity limited the comparability of the studies. 19 of the 32 included studies received industry funding, which may have biased their results. LIMITATIONS: It was not possible to perform a quantitative synthesis of the results due to the heterogeneity of the underlying studies. CONCLUSION: Differences in results between previous CEAs may have been driven by heterogeneity in modelling approaches, clinical input data, and potential funding biases. A more consistent evidence base and modelling approach would enhance the comparability between CEAs.
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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 | 0.023 | 0.094 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.025 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".