Information in the sample covariate distribution in prevalent cohorts
Bibliographic record
Abstract
Methods of estimation and inference about survival distributions based on length-biased samples are well-established. Comparatively little attention has been given to the assessment of covariate effects in the context of length-biased samples, but prevalent cohort studies often have this objective. We show that, like the survival distribution, the covariate distribution from a prevalent cohort study is length-biased, and that this distribution may contain parametric information about covariate effects on the survival time. As a result, a likelihood based on the joint distribution of the survival time and the covariates yields estimates of covariate effects which are at least as efficient as estimates arising from a traditional likelihood which conditions on covariate values in the length-biased sample. We also investigate the empirical bias of estimators arising from a joint likelihood when the population covariate distribution is misspecified. The asymptotic relative efficiencies and empirical biases under model misspecification are assessed for both proportional hazards and accelerated failure time models. The various methods considered are applied in an illustrative analysis of risk factors for death following onset of dementia using data collected in 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 | 0.089 | 0.331 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".