Testing for Monotone Trend in Recurrent Event Processes
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 Tests for the presence of monotone trends in recurrent event processes have been proposed and studied by many authors. However, a general concept of trend is elusive, and the dependence of the behavior of tests on the assumed definitions of "no trend" and "trend," and on the observation periods for the processes, has been largely ignored. We discuss these issues and study them through analytical results and simulation studies. The article also presents robust tests for trend across multiple processes, extends them to deal with interval-censored event times, and compares them and other well-known trend tests with respect to the issues mentioned. Keywords: Lewis–Robinson testRank testsRate functionsRepairable systemsRenewal processesRobust tests. ACKNOWLEDGMENTS This research was supported by the Natural Sciences and Engineering Research Council of Canada (RJC, JFL), by the Canadian Institutes for Health Research (RJC), and by Cancer Care Ontario and the Ontario Institute for Cancer Research (through funding provided by the Ministry of Health and Long-Term Care and the Ministry of Research & Innovation of the Government of Ontario) (CÇ).
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 | 0.000 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| 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