Checking stationarity of the incidence rate using prevalent cohort survival data
Bibliographic record
Abstract
When survival data are collected as part of a prevalent cohort study with follow-up, the recruited cases have already experienced their initiating event, say onset of a disease, and consequently the incidence process is only partially observed. Nevertheless, there are good reasons for interest in certain features of the underlying incidence process, for example whether or not it is stationary. Indeed, the well known relationship between incidence and prevalence, often used by epidemiologists, requires stationarity of the incidence rate for its validity. Also, the statistician can exploit stationarity of the incidence process by improving the efficiency of estimators in a prevalent cohort survival analysis. In addition, whether the incident rate is stationary is often in itself of central importance to medical and other researchers. We present here a necessary and sufficient condition for stationarity of the underlying incidence process, which uses only survival observations, possibly right censored, from a prevalent cohort study with follow-up. This leads to a simple graphical means of checking for the stationarity of the underlying incidence times by comparing the plots of two Kaplan-Meier estimates that are based on partially observed incidence times and follow-up survival data. We use our method to discuss the incidence rate of dementia in Canada between 1971 and 1991.
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.
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 | 0.038 | 0.262 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| 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".