casebase: An Alternative Framework for Survival Analysis and Comparison of Event Rates
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
In clinical studies of time-to-event data, a quantity of interest to the clinician is their patient's risk of an event. However, methods relying on time matching or risk-set sampling (including Cox regression) eliminate the baseline hazard from the estimating function. As a consequence, the focus has been on reporting hazard ratios instead of survival or cumulative incidence curves. Indeed, reporting patient risk or cumulative incidence requires a separate estimation of the baseline hazard. Using case-base sampling, Hanley & Miettinen (2009) explained how parametric hazard functions can be estimated in continuous-time using logistic regression. Their approach naturally leads to estimates of the survival or risk function that are smooth-in-time. In this paper, we present the casebase R package, a comprehensive and flexible toolkit for parametric survival analysis. We describe how the case-base framework can also be used in more complex settings: non-linear functions of time and non-proportional hazards, competing risks, and variable selection. Our package also includes an extensive array of visualization tools to complement the analysis. We illustrate all these features through three different case studies. * SRB and MT contributed equally to this work.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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