Economic evaluation of robot-assisted radical prostatectomy compared to open radical prostatectomy for prostate cancer treatment in Ontario, Canada
Bibliographic record
Abstract
INTRODUCTION: Recent health technology assessments (HTAs) of robot-assisted radical prostatectomy (RARP) in Ontario and Alberta, Canada, resulted in opposite recommendations, calling into question whether benefits of RARP offset the upfront investment. Therefore, the study objectives were to conduct a cost-utility analysis from a Canadian public payer perspective to determine the cost-effectiveness of RARP. METHODS: Using a 10-year time horizon, a five-state Markov model was developed to compare RARP to open radical prostatectomy (ORP). Clinical parameters were derived from Canadian observational studies and a recently published systematic review. Costs, resource utilization, and utility values from recent Canadian sources were used to populate the model. Results were presented in terms of increment costs per quality-adjusted life years (QALYs) gained. A probabilistic analysis was conducted, and uncertainty was represented using cost-effectiveness acceptability curves (CEACs). One-way sensitivity analyses were also conducted. Future costs and QALYs were discounted at 1.5%. RESULTS: Total cost of RARP and ORP were $47 033 and $45 332, respectively. Total estimated QALYs were 7.2047 and 7.1385 for RARP and ORP, respectively. The estimated incremental cost-utility ratio (ICUR) was $25 704 in the base-case analysis. At a willingness-to-pay threshold of $50 000 and $100 000 per QALY gained, the probability of RARP being cost-effective was 0.65 and 0.85, respectively. The model was most sensitive to the time horizon. CONCLUSIONS: The results of this analysis suggest that RARP is likely to be cost-effective in this Canadian patient population. The results are consistent with Alberta's HTA recommendation and other economic evaluations, but challenges Ontario's reimbursement decision.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".