A Systematic Literature Review of Health Utility Values in Breast Cancer
Bibliographic record
Abstract
BACKGROUND: Health utility values (HUVs) are important inputs to the cost-utility analysis of breast cancer interventions. PURPOSE: Provide a catalog of breast cancer-related published HUVs across different stages of breast cancer and treatment interventions. DATA SOURCES: Systematic searches of MEDLINE, MEDLINE In-Process, EMBASE, Web of Science, CINAHL, PsycINFO, EconLit, and Cochrane databases (2005-2017). STUDY SELECTION: Studies published in English that reported mean or median HUVs using direct or indirect methods of utility elicitation for breast cancer. DATA EXTRACTION: Independent reviewers extracted data on a preestablished and piloted form; disagreements were resolved through discussion. DATA ANALYSIS: Mixed-effects meta-regression using restricted maximum likelihood modeling was conducted for intervention type, stage of breast cancer, and typical clinical and treatment trajectory of breast cancer patients to assess the effect of study characteristics (i.e., sample size, utility elicitation method, and respondent type) on HUVs. DATA SYNTHESIS: = 52, 66%) derived HUVs using the EQ-5D. Patients with advanced-stage breast cancer (range, 0.08 to 0.82) reported lower HUVs as compared with patients with early-stage breast cancer (range, 0.58 to 0.99). The meta-regression analysis found that undergoing chemotherapy and surgery and radiation, being diagnosed with an advanced stage of breast cancer, and recurrent cancer were associated with lower HUVs. The members of the general public reported lower HUVs as compared with patients. LIMITATIONS: There was considerable heterogeneity in the study population, health states assessed, and utility elicitation methods. CONCLUSION: This review provides a catalog of published HUVs related to breast cancer. The substantial heterogeneity in the health utility studies makes it challenging for researchers to choose which HUVs to use in cost-utility analyses for breast cancer interventions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.076 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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; both teacher heads 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".