Analyzing Health-Related Quality of Life in the EVOLVE Trial
Bibliographic record
Abstract
BACKGROUND: The Evaluation of Cinacalcet HCl Therapy to Lower Cardiovascular Events (EVOLVE) clinical trial evaluated the effects of cinacalcet on clinical events in patients with secondary hyperparathyroidism (sHPT) who were on hemodialysis. Health-related quality of life (HRQoL) was assessed by a generic, preference-based health outcome measure (EQ-5D) at scheduled visits and after a study event. Here, we report the HRQoL analysis from EVOLVE. METHODS: We assessed changes in HRQoL from baseline to scheduled visits, and estimated the acute (3 mo) and chronic (beyond 3 mo) effects of sHPT-related events on HRQoL using generalized estimating equation analysis controlling for baseline HRQoL and randomized assignment. RESULTS: Data on HRQoL were available for 3547 of 3883 subjects, with 1650 events in the placebo and 1502 in the cinacalcet arm. At the study end, no difference in change from baseline HRQoL was observed in the direct comparison of EQ-5D by treatment arms. The regression analysis showed significant effects of events on HRQoL and a modest positive effect of cinacalcet. Estimated quality-adjusted life-year gains were of similar magnitude based on the observed data or the predictions from the model, with only a small gain in precision from the predicted analysis. CONCLUSIONS: By contrast with a conventional comparison, a regression analysis demonstrated large decrements in HRQoL after events and a modest improvement in HRQoL with cinacalcet. As randomized controlled trials are rarely powered to detect differences in HRQoL, a prespecified regression analysis may be acceptable to improve precision of the effects and understand their origin.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".