Cost-Effectiveness Analysis of Stereotactic Ablative Body Radiotherapy for the Treatment of Oligometastatic Tumors versus Standard of Care
Bibliographic record
Abstract
BACKGROUND: Recent clinical trial results reported that stereotactic radiotherapy (SABR) may improve survival for patients with oligometastatic (OM) cancer. Given that these results come from a phase II trial, there remains considerable uncertainty about this finding, and about the cost-effectiveness of SABR for patients with OM cancer. In this analysis, we estimate the cost-effectiveness of SABR for oligometastatic cancer patients. METHODS: A probabilistic time-dependent Markov model was constructed to simulate treatment of oligometastatic cancer patients over five- and ten-year time horizons. The primary data source was the phase II, Stereotactic Ablative Radiotherapy for the Comprehensive Treatment of Oligometastases (SABR-COMET )trial and supplemented with data from the literature. We estimated the effect of SABR and the standard of care (SoC) using quality-adjusted life-years (QALYs). Costs were measured from a provincial payer perspective (2018 Canadian dollars). RESULTS: In the reference case analysis (five-year time horizon), SABR was associated with additional incremental costs of CAD 38,487 and an incremental QALY gain of 0.84. This resulted in an incremental cost-effectiveness ratio (ICER) of CAD 45,726 per QALY gained. Over a ten-year time horizon, the increased uncertainty in the long-term effectiveness of SABR resulted in an ICER of CAD 291,544 per QALY gained. Estimates from the probabilistic analysis indicated that at a willingness-to-pay (WTP) threshold of CAD 50,000 and CAD 100,000 per QALY gained, there is 54% and 78% probability (respectively) that SABR would be cost-effective using the five-year time horizon. CONCLUSIONS: The adoption of SABR therapy requires a considerable upfront capital investment. Our results suggest that the cost-effectiveness of SABR is contingent on the uncertainty in the evidence base. Further clinical trials to confirm the effectiveness of SABR and research into the real-world costs associated with this treatment could reduce the uncertainty around implementation of the technology.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".