Health Utility Survival for Randomized Clinical Trials: Extensions and Statistical Properties
Bibliographic record
Abstract
Overall survival has been used as the primary endpoint for many randomized trials that aim to examine whether a new treatment is non-inferior to the standard treatment or placebo control. When a new treatment is indeed non-inferior in terms of survival, it may be important to assess other outcomes including health utility. However, analyzing health utility scores in a secondary analysis may have limited power since the primary objectives of the original study design may not include health utility. To comprehensively consider both survival and health utility, we developed a composite endpoint, HUS (Health Utility-adjusted Survival), which combines both survival and utility. HUS has been shown to be able to increase statistical power and potentially reduce the required sample size compared to the standard overall survival endpoint. Nevertheless, the asymptotic properties of the test statistics of the HUS endpoint have yet to be fully established. Besides that, the standard version of HUS cannot be applied to or has limited performance in certain scenarios, where extensions are needed. In this manuscript, we propose various methodological extensions of HUS and derive the asymptotic distributions of the test statistics. By comprehensive simulation studies and a data application using retrospective data based on a translational patient cohort in Princess Margaret Cancer Centre, we demonstrate the better efficiency and feasibility of HUS compared to different methods.
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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 | 0.493 | 0.653 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".