Bibliographic record
Abstract
The analysis of past developments of processes through dynamic covariates is useful to understand the present and future of processes generating recurrent events. In this study, we consider two essential features of recurrent event processes through dynamic models. These features are related to monotonic trends and clustering of recurrent events, and frequently seen in medical studies. We discuss the estimation of these features through dynamic models for event counts. We also focus on the settings in which unexplained excess heterogeneity is present in the data. Furthermore, we show that the violation of the strong assumption of independent gap times may introduce substantial bias in the estimation of these features with models for event counts. To address these issues, we apply a copula-based estimation method for the gap time models. Our approach does not rely on the strong independent gap time assumption, and provides a valid estimation of model parameters. We illustrate the methods developed in this study with data on repeated asthma attacks in children. Finally, we propose some goodness-of-fit procedures as future research.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".