Whether and How Disutilities of Adverse Events were Used in the Economic Evaluation of Drug Therapy for Cancer Treatment
Bibliographic record
Abstract
BACKGROUND: The disutilities of adverse events (AEs) are important inputs for cost-utility analysis (CUA), reflecting the impacts of AEs on health outcomes. Health technology assessment institutions and scholars have proposed recommendations for applying disutility values in economic evaluations. OBJECTIVES: This study aimed to identify the current use of disutilities of AEs as model parameters in the CUA of cancer drug therapy and to compare the discrepancies between the use of disutilities and published recommendations. METHODS: A systematic search was conducted on the PubMed, Web of Science, and Cochrane Library databases, as well as the official websites of the National Institute for Health and Care Research (NIHR), the Institute for Clinical and Economic Review (ICER), the Institute for Quality and Efficiency in Health Care (IQWiG), the Canadian Agency for Drugs and Technologies in Health (CADTH), and the Centre for Reviews and Dissemination (CRD) for CUAs of drug therapy for cancer published in English from January 2019 to April 2022. Information about the use of disutilities of AEs (whether and how disutilities were used, or why they were not used) in selected studies was extracted and compared with published recommendations. Descriptive analyses were used to summarize the results. RESULTS: A total of 467 CUAs were included, 54% (254/467) of which included disutilities of AEs in their model. The proportion that included these disutilities increased from 2019 to 2021, ranging from 47% (51/107) to 61% (116/190). Only 6% (15/254) of the CUAs using disutilities of AEs considered all five recommendations about the justification for inclusion and exclusion, description of values and sources, grades of AEs, calculation, and uncertainty analyses. Only 15% (72/467) provided a clear justification for inclusion and exclusion of disutilities of AEs, and 7% (17/254) did not provide values or sources. In total, 69% (175/254) of the analyses focused on AEs of grade 3 or greater, and 11% (28/254) applied utility decrements for grades 1 and 2. Disutilities of AEs were generally calculated using the incidence rates, which were clearly stated in 49% (65/132) of the analyses. Uncertainty analyses were conducted in 84% (214/254) of the CUAs. CONCLUSIONS: The current use of disutilities of AEs in CUAs shows some discrepancies with recommendations proposed in the literature. One is that detailed information about the use of disutilities of AEs was not reported and the other is that essential methods to analyze the impact of AEs on quality-adjusted life-years were not thoroughly conducted. Therefore, it is suggested that researchers should attach importance to the impact of AEs on health-related quality of life. Furthermore, an application process was developed for the disutilities of AEs to remind and guide researchers to correctly use the disutilities of AEs as parameters in the decision-analytic model.
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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 | 0.210 | 0.486 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.022 |
| Bibliometrics | 0.016 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".