Identification of evidence for key parameters for health economic models used to evaluate the cost-effectiveness of health care technologies
Bibliographic record
Abstract
Objectives. The objective of this PhD study was to compare different information retrieval methods that can be used to identify health economic model inputs. Methods. Existing search methods were compared to two alternatives (iterative searching and rapid review), using three health technology assessment (HTA) case studies in ulcerative colitis, thyroid cancer and breast cancer tumour profiling risk stratification. Key criteria for selecting the case studies were the availability of an executable Excel model. Two model inputs were chosen to be tested: health state utilities and baseline risk of clinical events. Usual practice searches were updated, and alternative search methods (iterative searching and rapid review) were conducted, and the differences in model inputs identified by each search approach were analysed. Differences were evaluated in terms of time taken to search, sensitivity, burden (precision and number needed to read) and relevance of identified information. The identified model input values were tested in an executable health economic model, and, when feasible, the model results were compared in order to understand the impact on model outputs. Results. Usual practice for identifying health state utility inputs was a systematic review in all except one case study, where a previous health economic model output was used. In all case studies the alternative search methods were mostly less resource intensive and resulted in identical or similar model inputs, with no changes to the conclusions drawn from the health economic model. Usual practice for identifying baseline risk of clinical events varied from no recorded search steps to a systematic review. When the effort for usual practice could not be estimated due to the lack of recorded search steps, the time difference could not be estimated. However, it was clear that applying alternative search methods increased the transparency. Conclusions. Alternative search methods were more efficient and more transparent than established search methods, without impacting the health economic model conclusions. Further case studies are required to examine whether this conclusion remains generalisable, and applies to other health economic model inputs.
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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 | 0.273 | 0.702 |
| Meta-epidemiology (narrow) | 0.006 | 0.004 |
| Meta-epidemiology (broad) | 0.017 | 0.042 |
| Bibliometrics | 0.023 | 0.017 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.017 | 0.012 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".