Current status observation of a three‐state counting process with application to simultaneous accurate and diluted HIV test data
Bibliographic record
Abstract
Abstract The authors examine multistate current status data defined by two survival times of interest where one only observes whether or not each of the individual survival times exceed a common observed monitoring time. An individual can therefore belong to one of three states. The authors are interested in whether current status information on the second event can be used to improve estimation of the distribution function of time to the first event. For both single and multiple monitoring time scenarios, in the fully nonparametric setting, one cannot improve the naïve estimator, using information on the first event only, when estimating “smooth” functionals of the distribution of time to the first event (van der Laan & Jewell, 2003). Therefore, improving the naïve estimator is examined when parametric assumptions about the waiting time between the two events are made. For situations where this waiting time is modifiable by design, the issue of determining the optimal length of the waiting time for estimation of the cumulative hazard of the distribution of time to the first event in the recent past is also addressed. The ideas are motivated by and applied to an example on simultaneous accurate and diluted assay HIV test data. The Canadian Journal of Statistics 39: 475–487; 2011 © 2011 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 | 0.012 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".