Cost-utility of geriatric assessment (GA) in older adults with cancer: A model-based economic evaluation of four randomized controlled trials (RCTs).
Bibliographic record
Abstract
1509 Background: Geriatric assessment (GA) is a guideline-recommended approach to optimize cancer management in older adults undergoing chemotherapy. Our recent cost-utility analysis of the published 5C (Clinical and Cost-effectiveness of a Comprehensive geriatric assessment and management for Canadian elders with Cancer) RCT comparing GA and management (GAM) with usual care in older adults with cancer did not demonstrate cost-effectiveness overall. However, the trial was limited by <5% of study participants receiving GA prior to starting treatment. Three other GAM RCTs (GAIN, GAP-70, and INTEGERATE) have recently been published with evidence of efficacy on clinically relevant endpoints. Whether these are more cost effective than 5C is unclear. We evaluated the cost-effectiveness of GAM versus usual care in older adults with cancer using a decision model under a range of plausible scenarios representing the 4 trials. Methods: We performed cost-effectiveness analyses using the healthcare payer perspective and a 12-month time horizon. We incorporated Canadian costs and utility data from 5C, and used intervention details and effectiveness data from the three RCTs. We reported healthcare costs per quality-adjusted life year (QALY) and the incremental net monetary benefit (INMB) using a $50,000 per QALY threshold. In scenario analyses we examined the main cost drivers. Results: Across trials, the average QALY per patient ranged 0.577–0.662 for GA and 0.606–0.665 for UC, and the average total costs $31,234–$39,432 for GA and $29,261–$41,756 for UC. Chemotherapy expenses accounted for 46%-66% of total costs across trials. The INTEGERATE trial had a positive INMB of $6,074. The GAIN and GAP-70 trials had negative INMB value of -$2,123 and -$1,172, respectively. In comparison, in 5C, the total costs were $39,812 and $37,450 for GAM and UC, respectively, and QALYs were 0.728 and 0.751, respectively; the INMB was $-2,713. Conclusions: Trial results and the associated model of care from INTEGERATE suggested a positive net monetary benefit, primarily driven by reduced hospitalization. Evaluation of cost-effectiveness under a range of plausible scenarios from RCTs can provide important insights about GAM. Our results add to the growing data supporting the need to implement GAM in older adults with cancer starting chemotherapy and argue for its cost effectiveness under specific scenarios. Future trials should include hospitalization outcomes. Future economic analyses need to accurately capture chemotherapy costs.
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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 | 0.046 | 0.081 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.022 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| 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".