Adjusting for Drug Wastage in Economic Evaluations of New Therapies for Hematologic Malignancies: A Systematic Review
Why this work is in the frame
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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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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 | 0.083 | 0.090 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.010 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 it