Health economics of targeted intraoperative radiotherapy (TARGIT-IORT) for early breast cancer: a cost-effectiveness analysis in the United Kingdom
Bibliographic record
Abstract
OBJECTIVE: The clinical effectiveness of targeted intraoperative radiotherapy (TARGIT-IORT) has been confirmed in the randomised TARGIT-A (targeted intraoperative radiotherapy-alone) trial to be similar to a several weeks' course of whole-breast external-beam radiation therapy (EBRT) in patients with early breast cancer. This study aims to determine the cost-effectiveness of TARGIT-IORT to inform policy decisions about its wider implementation. SETTING: TARGIT-A randomised clinical trial (ISRCTN34086741) which compared TARGIT with traditional EBRT and found similar breast cancer control, particularly when TARGIT was given simultaneously with lumpectomy. METHODS: Cost-utility analysis using decision analytic modelling by a Markov model. A cost-effectiveness Markov model was developed using TreeAge Pro V.2015. The decision analytic model compared two strategies of radiotherapy for breast cancer in a hypothetical cohort of patients with early breast cancer based on the published health state transition probability data from the TARGIT-A trial. Analysis was performed for UK setting and National Health Service (NHS) healthcare payer's perspective using NHS cost data and treatment outcomes were simulated for both strategies for a time horizon of 10 years. Model health state utilities were drawn from the published literature. Future costs and effects were discounted at the rate of 3.5%. To address uncertainty, one-way and probabilistic sensitivity analyses were performed. MAIN OUTCOME MEASURES: Quality-adjusted life-years (QALYs). RESULTS: In the base case analysis, TARGIT-IORT was a highly cost-effective strategy yielding health gain at a lower cost than its comparator EBRT. Discounted TARGIT-IORT and EBRT costs for the time horizon of 10 years were £12 455 and £13 280, respectively. TARGIT-IORT gained 0.18 incremental QALY as the discounted QALYs gained by TARGIT-IORT were 8.15 and by EBRT were 7.97 showing TARGIT-IORT as a dominant strategy over EBRT. Model outputs were robust to one-way and probabilistic sensitivity analyses. CONCLUSIONS: TARGIT-IORT is a dominant strategy over EBRT, being less costly and producing higher QALY gain. TRIAL REGISTRATION NUMBER: ISRCTN34086741; post results.
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".