Developing a Comprehensive Framework for Cost-Effectiveness Evaluation in Metastatic Castration-Sensitive Prostate Cancer: Insights from a Systematic Review
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Metastatic castration-sensitive prostate cancer (mCSPC) imposes a significant economic burden and necessitates more cost-effective treatment strategies. The variability among the components of published economic evaluation models leads to methodological inconsistencies, underscoring the need for an optimal framework to minimise unwarranted structural variation. This paper reviews existing economic evaluations, establishes a comprehensive framework and aims to support future economic evaluations and decision-making in mCSPC. METHODS: A systematic literature review (SLR) was conducted to identify relevant economic evaluations in mCSPC. Health technology assessments (HTAs) by the National Institute for Health and Care Excellence, and Canada's Drug Agency were reviewed to gather insights on critiques and limitations. On the basis of these findings, a comprehensive cost-effectiveness modelling framework was established. Furthermore, two additional SLRs were conducted to identify cost and resource utilisation inputs, as well as health state utility scores derived from published studies and HTA assessments. RESULTS: Markov models and partitioned survival models (PSMs) were commonly reported in literature and published HTA evaluations. Despite the strong precedence of PSMs, we propose an optimal framework for mCSPC utilising a semi-Markov structure. This approach offers increased flexibility, allowing transition rates from progressed states to depend on time since progression occurred. We also present key sources of cost and utility data identified in the SLR. DISCUSSION: This work aligns with methodologies recommended by the Innovative Medicine Initiative (IMI) PIONEER external group and published studies. The optimal framework, including healthcare resource utilisation and utility data, consolidates existing modelling precedents in mCSPC and will assist the cost-effectiveness assessment of treatments for this condition.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".