Return-on-Investment in Health Economic Evaluation: An Exploratory Analysis
Bibliographic record
Abstract
OBJECTIVES: This article explores issues of methodological rigor and threshold development for return-on-investment (ROI) measures. Although economic evaluation methods such as cost-effectiveness, cost-utility analysis, and cost-benefit analyses have standard guidelines that allow for consistent comparison across studies, these are sorely missing with respect to ROI. METHODS: We use economic concepts of opportunity cost, Pareto optimality, and fairness to propose initial ROI threshold measures and help explore how guidelines for future calculation of these measures might be established. RESULTS: This article proposes an initial threshold of 0.08 for ROI calculations that monetize health benefits and -0.47 for those that fail to do so based on the economic concept of opportunity cost. This article also flags concern regarding the overall methodology for calculating ROI potentially resulting in suboptimal resource allocation and ethical concerns with bias in the way ROI measures may direct resources. CONCLUSIONS: There are a number of methodological shortcomings with regard to ROI measures that partially explains the orders of magnitude greater variation observed in this measure compared with other economic evaluation measures when examining the full scope of health interventions. The establishment of thresholds and future guidelines for calculation should narrow this variation somewhat and help to avoid impinging upon Pareto optimality and fairness. Such modifications should help increase the utility of ROI measures in the future.
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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 | 0.037 | 0.167 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".