Comparison of imputation methods for interval censored time‐to‐event data in joint modelling of tree growth and mortality
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The authors link time‐to‐event models with longitudinal models through shared latent variables when the time of the event of interest is known only to lie within an interval. The context of tree growth and mortality studies presents a natural application of shared parameter joint modelling where a latent feature of each tree impacts both mortality and growth. The authors' developments are motivated by such an application, with the additional caveat that event‐times are not known exactly, since the trees are subject to intermittent observation, with the time between measurements extending into decades or longer. Such interval censoring is a common occurrence in similar long‐term experiments in resource management, ecology and health research. The additional numerical complexity resulting from interval censored time‐to‐event data often makes inference for joint models prohibitive. The authors examine properties of three event‐time imputation methods that enable application of now standard joint modelling techniques to interval censored time‐to‐event data. The imputation techniques include the midpoint method, a kernel smoothing method, and a backsolve method which incorporates information from the longitudinal trajectory. Joint analysis of a designed, long‐term, forestry experiment is presented, accompanied by a simulation study investigating the properties of the three event‐time imputation techniques. The Canadian Journal of Statistics 39: 438–457; 2011 © 2011 Statistical Society of Canada
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it