Bibliographic record
Abstract
Interval-censoring occurs in survival analysis when the time until an event of interest is not known precisely (and instead, only is known to fall into a particular interval). Such censoring commonly is produced when periodic assessments (usually clinical or laboratory examinations) are used to assess if the event has occurred. My objectives were to raise awareness about interval-censoring including its existence, the potential ramifications of ignoring its existence, the different types of interval-censored data, and the analytical methods to analyze such data (including availability in standard statistical software). Asynchronous interval-censored survival analysis was demonstrated by parametric evaluation of risk factors for the time to first detected shedding of Salmonella muenster (identified by repeated periodic fecal cultures) for a herd of dairy cows. These results were compared with those from survival analyses which ignored or approximated the interval-censoring. Ignoring or approximating the asynchronous interval-censoring in the survival analysis generally resulted in the risk factors' regression coefficients having the same signs and a decrease (often >50%) in their absolute size. All the standard errors from the three methods of approximating the interval-censoring were <40% of their interval-censored counterparts. The conclusions drawn from the asynchronous interval-censored analysis versus those from the approximations varied dramatically. (The general conclusion from the approximations was that none of the risk factors for this example warranted further consideration.) That ignoring or approximating the left- and interval-censored nature of the dependent variable resulted in biased results was consistent with the literature. In the currently available asynchronous interval-censored models, the inclusion of time-dependent covariates that vary continuously is awkward. Statistical models for the semi-parametric estimation of asynchronous interval-censored survival analysis are not generally available in standard statistical software.
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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 | 0.019 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| 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".