On estimands arising from misspecified semiparametric rate‐based analysis of recurrent episodic conditions
Why this work is in the frame
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Bibliographic record
Abstract
Marginal rate-based analyses are widely used for the analysis of recurrent events in clinical trials. In many areas of application, the events are not instantaneous but rather signal the onset of a symptomatic episode representing a recurrent infection, respiratory exacerbation, or bout of acute depression. In rate-based analyses, it is unclear how to best handle the time during which individuals are experiencing symptoms and hence are not at risk. We derive the limiting value of the Nelson-Aalen estimator and estimators of the regression coefficients under a semiparametric rate-based model in terms of an underlying two-state process. We investigate the impact of the distribution of the episode durations, heterogeneity, and dependence on the asymptotic and finite sample properties of standard estimators. We also consider the impact of these features on power in trials designed to test intervention effects on rate functions. An application to a trial of individuals with herpes simplex virus is given for illustration.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.007 | 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