Systematic Review and Meta-Analysis of Community- and Choice-Based Health State Utility Values for Lung Cancer
Bibliographic record
Abstract
BACKGROUND: Using appropriate health state utility values (HSUVs) is critical for economic evaluation of new lung cancer interventions, such as low-dose computed tomography screening and immunotherapy. Therefore, we provide a systematic review and meta-analysis of community- and choice-based HSUVs for lung cancer. METHODS: On 6 March 2017, we conducted a systematic search of the following databases: Embase, Ovid MEDLINE, Web of Science, Cochrane CENTRAL, Google Scholar, and the School of Health and Related Research Health Utility Database. The search was updated on 17 April 2019. Studies reporting mean or median lung cancer-specific HSUVs including a measure of variance were included and assessed for relevance and validity. Studies with high relevance (i.e. community- and choice-based) were further analysed. Mean HSUVs were pooled using random-effects models for all stages, stages I-II, and stages III-IV. For studies with a control group, we calculated the disutility due to lung cancer. A sensitivity analysis included only the methodologically most comparable studies (i.e. using the EQ-5D instrument and matching tariff). Subgroup analyses were conducted by time to death, histology, sex, age, treatment modality, treatment line, and progression status. RESULTS: = 92-99%). Disutility due to lung cancer ranged from 0.11 (95% CI 0.05-0.17) to 0.27 (95% CI 0.18-0.36). In the sensitivity analysis with the methodologically most comparable studies, stage-specific HSUVs varied by country. Such studies were only identified for Canada, China, Spain, the UK, the USA, Denmark, Germany, and Thailand. In the subgroup analysis by time to death, HSUVs for metastatic non-small-cell lung cancer ranged from 0.83 (95% CI 0.82-0.85) at ≥ 360 days from death to 0.56 (95% CI 0.46-0.66) at < 30 days from death. Among patients with metastatic non-small-cell lung cancer, HSUVs were lower for those receiving third- or fourth-line treatment and for those with progressed disease. Results of subgroup analyses by histology, sex, age, and treatment modality were ambiguous. CONCLUSIONS: The presented evidence supports the use of stage- and country-specific HSUVs. However, such HSUVs are unavailable for most countries. Therefore, our pooled HSUVs may provide the best available stage-specific HSUVs for most countries. For metastatic non-small-cell lung cancer, adjusting for the decreased HSUVs in the last year of life may be considered, as may further stratification of HSUVs by treatment line or progression status. If required, HSUVs for other health states may be identified using our comprehensive breakdown of study characteristics.
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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 | 0.034 | 0.103 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.021 | 0.043 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".