Bibliographic record
Abstract
[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] Biased sampling arises when the observations are not randomly selected from the target population. When the sampling probability is proportional to the underlying outcome of interest, this is known as length-biased sampling. Length-biased sampling has been well recognized in economics, industrial reliability, etiology applications, epidemiological studies and cancer screening trials. Right-censored time-to-event data are often observed from a cohort of prevalent cases that are subject to length-biased sampling, which are termed as length-biased and right-censored data. It has a unique data structure different from traditional survival data and thus requires different inference methods for both nonparametric and semiparametric estimations. In this thesis, we will exploit these unique aspects and discuss the statistical analysis of length-biased and right-censored data. The first part of this dissertation discusses a goodness-of-fit test for checking the parametric model with length-biased and right-censored data. The second part of this dissertation considers the regression analysis of length-biased and right-censored data in the context of the novel two sample short-term and long-term hazard ratios model. The third part of this dissertation proposes an inverse probability weighted (IPW) method and a reweighted method for estimating the regression parameters in the Cox model with missing covariates under length-biased sampling. The performance of the proposed approaches are demonstrated through simulation studies and we apply the approaches to the survival data from the Canadian Study of Health and Aging(CSHA).
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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 | 0.059 | 0.263 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".