Methods for the Economic Evaluation of Health Care Programmes
Bibliographic record
Abstract
Abstract The highly successful textbook Methods for the Economic Evaluation of Health Care is now available in its third edition. Over the years it has become the standard textbook in the field world-wide. It mirrors the huge expansion of the field of economic evaluation in health care. This new edition builds on the strengths of previous editions being clearly written in a style accessible to a wide readership. Key methodological principles are outlined using a critical appraisal checklist that can be applied to any published study. The methodological features of the basic forms of analysis are then explained in more detail with special emphasis of the latest views on productivity costs, the characterization of uncertainty and the concept of net benefit. The book has been greatly revised and expanded especially concerning analyzing patient-level data and decision-analytic modeling. There is discussion of new methodological approaches, including cost effectiveness acceptability curves, net benefit regression, probalistic sensitivity analysis and value of information analysis. There is an expanded chapter on the use of economic evaluation, including discussion of the use of cost-effectiveness thresholds, equity considerations and the transferability of economic data. This new edition is required for anyone commissioning, undertaking or using economic evaluations in health care, and will be popular with health service professionals, health economists, pharmacists and health care decision makers. It is especially relevant for those taking pharmacoeconomics courses.
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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 | 0.052 | 0.122 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.031 | 0.004 |
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".