Assay-guided treatment sequencing in chronic lymphocytic leukemia (CLL): a cost-effectiveness analysis
Bibliographic record
Abstract
Costly targeted cancer treatments challenge publicly-funded healthcare systems seeking to align expected benefit with value for money. In 2021, The Canadian Agency for Drugs and Technologies in Health (CADTH) published a provisional funding algorithm for risk-based treatment of chronic lymphocytic leukemia (CLL). We estimate the cost-effectiveness of this algorithm against current standard of care. We constructed a probabilistic Markov model comparing next generation sequencing (NGS) assay-guided front-line treatment of acalabrutinib versus venetoclax with obinutuzumab to a comparator wherein patients initiate acalabrutinib. The primary outcome was the incremental cost-effectiveness ratio (ICER) per quality-adjusted life-year (QALY) gained. Analyses were conducted from the British Columbia healthcare system perspective, with outcomes discounted at 1.5%. Assay informed treatment for patients with CLL resulted in an incremental cost effectiveness ratio of $18,040 (95% CI $16,491-$19,501) per quality adjusted life-year (QALY) gained. The probability of the NGS guided treatment algorithm being cost effective was 80% at a willingness to pay threshold of $50,000 and a corresponding ICER of $18,040. Assay-guided treatment sequencing adds additional costs to healthcare but may be a cost-effective intervention for adult patients with CLL. Integration of real-world evidence would improve the validity and reliability of model estimated for decision-makers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".