Adjusting for Drug Wastage in Economic Evaluations of New Therapies for Hematologic Malignancies: A Systematic Review
Bibliographic record
Abstract
PURPOSE: As costs of cancer care rise, there has been a shift to focus on value. Drug wastage affects costs to patients and health care systems without adding value. Historically, cost-effectiveness analyses have used models that assume no drug wastage; however, this may not reflect real-world practices. We sought to identify the frequency of drug wastage modeling in economic evaluations of modern parenteral therapies for hematologic malignancies. METHODS: We conducted a systematic literature review of economic evaluations of new US Food and Drug Administration-approved parenteral chemotherapies with indications for the treatment of hematologic malignancies. The primary outcome of interest was the proportion of studies that modeled drug wastage in base-case analyses. If wastage was considered in primary analyses, we reported the impact of wastage on incremental cost-effectiveness ratios (ICERs) and drug acquisition costs. RESULTS: Wastage was considered in base-case analyses in less than one third of all publications reviewed (12 of 38; 32%). Of these, two studies went on to complete sensitivity analyses and reported significant changes in the calculated ICER as a result. In one study, the ICER increased by 32%, and in the second, accounting for wastage changed a positive ICER to a dominant result. CONCLUSION: Potential costs associated with drug wastage are considered in only one third of modern cost-effectiveness models. The impact of wastage on calculated ICERs and drug acquisition costs is potentially substantial. The modeling of wastage in base-case and sensitivity analyses is recommended for future economic evaluations of new intravenous therapies for hematologic malignancies.
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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 | 0.044 | 0.181 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.026 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".