Semiparametric methods for survival analysis of case‐control data subject to dependent censoring
Bibliographic record
Abstract
Abstract Case‐control sampling can be an efficient and cost‐saving study design, wherein subjects are selected into the study based on the outcome of interest. It was established long ago that proportional hazards regression can be applied to case‐control data. However, each of the various estimation techniques available assumes that failure times are independently censored. Since independent censoring is often violated in observational studies, we propose methods for Cox regression analysis of survival data obtained through case‐control sampling, but subject to dependent censoring. The proposed methods are based on weighted estimating equations, with separate inverse weights used to account for the case‐control sampling and to correct for dependent censoring. The proposed estimators are shown to be consistent and asymptotically normal, and consistent estimators of the asymptotic covariance matrices are derived. Finite‐sample properties of the proposed estimators are examined through simulation studies. The methods are illustrated through an analysis of pre‐transplant mortality among end‐stage liver disease patients obtained from a national organ failure registry.The Canadian Journal of Statistics42: 365–383; 2014 © 2014 Statistical Society of Canada
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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 | 0.044 | 0.167 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".