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Real-World Cost-Effectiveness Analysis: How Much Uncertainty Is in the Results?
- Source :
- Current Oncology; Volume 30; Issue 4; Pages: 4078-4093, Current oncology (Toronto, Ont.), vol 30, iss 4
- Publication Year :
- 2023
- Publisher :
- MDPI AG, 2023.
-
Abstract
- Cost-effectiveness analyses of new cancer treatments in real-world settings (e.g., post-clinical trials) inform healthcare decision makers about their healthcare investments for patient populations. The results of these analyses are often, though not always, presented with statistical uncertainty. This paper identifies five ways to characterize statistical uncertainty: (1) a 95% confidence interval (CI) for the incremental cost-effectiveness ratio (ICER); (2) a 95% CI for the incremental net benefit (INB); (3) an INB by willingness-to-pay (WTP) plot; (4) a cost-effectiveness acceptability curve (CEAC); and (5) a cost-effectiveness scatterplot. It also explores their usage in 22 articles previously identified by a rapid review of real-world cost effectiveness of novel cancer treatments. Seventy-seven percent of these articles presented uncertainty results. The majority those papers (59%) used administrative data to inform their analyses while the remaining were conducted using models. Cost-effectiveness scatterplots were the most commonly used method (34.3%), with 40% indicating high levels of statistical uncertainty, suggesting the possibility of a qualitatively different result from the estimate given. Understanding the necessity for and the meaning of uncertainty in real-world cost-effectiveness analysis will strengthen knowledge translation efforts to improve patient outcomes in an efficient manner.
- Subjects :
- Comparative Effectiveness Research
economic evaluation
cost effectiveness
Cost-Benefit Analysis
Cost-Effectiveness Analysis
real-world interventions
Oncology and Carcinogenesis
Uncertainty
healthcare
cancer interventions
Good Health and Well Being
Cost Effectiveness Research
statistics
Clinical Research
cancer
uncertainty
Humans
Oncology & Carcinogenesis
Subjects
Details
- ISSN :
- 17187729
- Volume :
- 30
- Database :
- OpenAIRE
- Journal :
- Current Oncology
- Accession number :
- edsair.doi.dedup.....ed9874420345973e0db3e64b28572bf7