A nonparametric test for equality of survival medians using right-censored prevalent cohort survival data
Bibliographic record
Abstract
The median is a robust summary commonly used for comparison between populations. The existing literature falls short in testing for equality of survival medians when the collected data do not form representative samples from their respective target populations and are subject to right censoring. Such data commonly occur in prevalent cohort studies with follow-up. We consider a particular case where the disease under study is stable, that is, the incidence rate of the disease is stable. It is known that survival data collected on diseased cases, when the disease under study is stable, form a length-biased sample from the target population. We fill the gap for the particular case of length-biased right-censored survival data by proposing a large-sample test using the nonparametric maximum likelihood estimator of the survivor function in the target population. The small sample performance of the proposed test statistic is studied via simulation. We apply the proposed method to test for differences in survival medians of Alzheimer's disease and dementia groups using the survival data collected as part of the Canadian Study of Health and Aging.
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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 | 0.020 | 0.126 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".