Dimensions Used in Instruments for QALY Calculation: A Systematic Review
Bibliographic record
Abstract
Economic assessment is of utmost importance in the healthcare decision-making process. The quality-adjusted life-year (QALY) concept provides a rare opportunity to combine two crucial aspects of health, i.e., mortality and morbidity, into a single index to perform cost-utility comparison. Today, many tools are available to measure morbidity in terms of health-related quality of life (HRQoL) and a large literature describes how to use them. Knowing their characteristics and development process is a key point for elaborating, adapting, or selecting the most well-suited instrument for further needs. In this aim, we conducted a systematic review on instruments used for QALY calculation, and 46 studies were selected after searches in four databases: Medline EBSCO, Scopus, ScienceDirect, and PubMed. The search procedure was done to identify all relevant publications up to 18 June 2020. We mainly focused on the type of instrument developed (i.e., generic or specific), the number and the nature of dimensions and levels used, the elicitation method and the model selected to determine utility scores, and the instrument and algorithm validation methods. Results show that studies dealing with the development of specific instruments were mostly motivated by the inappropriateness of generic instruments in their field. For the dimensions' and levels' selection, item response theory, Rasch analysis, and literature review were mostly used. Dimensions and levels were validated by methods like the Loevinger H, the standardised response mean, or discussions with experts in the field. The time trade-off method was the most widely used elicitation method, followed by the visual analogue scale. Random effects regression models were frequently used in determining utility scores.
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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 | 0.029 | 0.130 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.014 |
| Bibliometrics | 0.021 | 0.024 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".