Health state utility values: A description of their development and application for rheumatic diseases
Bibliographic record
Abstract
Introduction The primary outcomes of most clinical studies in rheumatology are chosen as the most relevant and meaningful for the clinical community. However, they have restricted value in assisting policymakers considering resource-allocation decisions. Although clinical outcomes might constitute important results in a rheumatology trial, their use in economic evaluation is confined to cost-effectiveness analysis where outcomes are measured in units that are relevant to the condition under investigation. Comparisons between cost-effectiveness studies are restricted because the outcomes are typically measured in units that differ from study to study. Comparison across therapeutic areas becomes almost impossible. Therefore, cost-utility analysis (CUA; a distinct form of cost-effectiveness analysis) in which outcomes are measured in terms of a standard unit metric that combines information on the quantity and quality of life, the quality-adjusted life year (QALY), is important (1,2). The QALY requires data that express health-related quality of life (HRQOL) in the form of a single value, known as a health state utility value (HSUV), which is scored on a scale that assigns a value of 1 to a state equivalent to full health and 0 to a state equivalent to death (3). Although the most recent studies in rheumatology have used some form of an HRQOL questionnaire, such as the Short Form 36, Health Assessment Questionnaire (HAQ), or Western Ontario and McMaster Universities Osteoarthritis Index, these questionnaires typically measure and summarize a number of aspects of quality of life as a profile based on the responses. However, none of these instruments alone can be used to obtain an HSUV and therefore they are not amenable for economic evaluation. To obtain an HSUV requires the incorporation of a preference weight (3). The resulting values can be used to compare the general population preferences for different disease states both within and across diseases. When this is linked to the effect of an intervention, policymakers tasked with improving the outcomes of the whole population can allocate resources accordingly. It is from this context that economic evaluation becomes important: by identifying what gains in HSUVs (and life years) can be achieved by new interventions and at what additional cost. HSUVs have been described, analyzed, and reported in the rheumatology literature for more than 10 years (4). With the rising cost of health care (5), the use of economic evaluations has escalated, hence the rising interest in the methods and results of HSUV measures. However, although the motivation for using HSUVs is clear, the issues surrounding their development and use, predominantly the research of economists, is less well understood. Policies informed by these methods can impact the treatments available to physicians, and ultimately the patients’ wellbeing. We consequently reviewed the rheumatologic literature with the objective of identifying and addressing key issues and concepts in the valuation of HSUVs and then reported their application in rheumatology. We make recommendations for persons wanting to obtain values in the future and highlight issues requiring further research.
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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".