A methodological guide for implementing and interpreting results of probabilistic analysis
Bibliographic record
Abstract
INTRODUCTION: Probabilistic analysis, also referred to as probabilistic sensitivity analysis (PSA), is used extensively in cost-effectiveness evaluations of health technologies. We present methodological guidance for implementing probabilistic analysis and interpreting its results for policy and decision-making. METHODS: We review the methodological issues related to common practices in probabilistic analysis, explore aspects that are currently not widely addressed in the health economics literature, and provide an overview of recent methodological developments. RESULTS: We use examples to highlight the advantages and disadvantages of common tools used for presenting probabilistic analysis results, including the cost-effectiveness acceptability curve (CEAC), cost-effectiveness acceptability frontier (CEAF), and value of information (VOI) analysis. We raise and address issues related to using Monte Carlo standard error to determine the number of iterations required, the implications of large uncertainty, and the credibility and meaningfulness of small differences in quality-adjusted life-years (QALYs). We then discuss evolving methods in probabilistic analysis, cautious uses of probabilistic analysis, and factors impacting parameter uncertainty. CONCLUSIONS: A deeper understanding of probabilistic analysis methods enables health economists and decision-makers to more effectively address and interpret parameter uncertainty in health economic evaluations, which is essential for making informed policy decisions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.140 | 0.336 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.036 | 0.023 |
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".