A Cost–Utility Analysis of Atezolizumab in the Second-Line Treatment of Patients with Metastatic Bladder Cancer
Bibliographic record
Abstract
Background: Despite initial promising results, the IMvigor211 clinical trial failed to demonstrate an overall survival (os) benefit for atezolizumab compared with chemotherapy as second-line treatment for metastatic bladder cancer (mbc). However, given lessened adverse events (aes) and preserved quality of life (qol) with atezolizumab, there might still be investment value. To evaluate that potential value, we conducted a cost-utility analysis (cua) of atezolizumab compared with chemotherapy from the perspective of the Canadian health care payer. Methods: A partitioned survival model was used to evaluate atezolizumab compared with chemotherapy over a lifetime horizon (5 years). The base-case analysis was conducted for the intention-to-treat (itt) population, with additional scenario analyses for subgroups by IMvigor-defined PD-L1 status. Health outcomes were evaluated through life-year gains and quality-adjusted life-years (qalys). Cost estimates in 2018 Canadian dollars for systemic treatment, aes, and end-of-life care were incorporated. The incremental cost-effectiveness ratio (icer) was used to compare treatment strategies. Parameter and model uncertainty were assessed through sensitivity and scenario analyses. Per Canadian guidelines, cost and effectiveness were discounted at 1.5%. Results: For the itt population, the expected qalys for atezolizumab and chemotherapy were 0.75 and 0.56, with expected costs of $90,290 and $8,466 respectively. The resultant icer for atezolizumab compared with chemotherapy was $430,652 per qaly. Scenario analysis of patients with PD-L1 expression levels of 5% or greater led to a lower icer ($334,387 per qaly). Scenario analysis of observed compared with expected benefits demonstrated a higher icer, with a shorter time horizon ($928,950 per qaly). Conclusions: Despite lessened aes and preserved qol, atezolizumab is not considered cost-effective for the second-line treatment of mbc.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 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 | 0.002 |
| 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".