Variable Selection for Progressive Multistate Processes Under Intermittent Observation
Why this work is in the frame
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Bibliographic record
Abstract
Multistate models offer a natural framework for studying many chronic disease processes. Interest often lies in identifying which among a large list of candidate variables play a role in the progression of such processes. We consider the problem of variable selection for progressive multistate processes under intermittent observation based on penalized log-likelihood. An Expectation-Maximization (EM) algorithm is developed such that the maximization step can exploit existing software for penalized Poisson regression thereby allowing for the use of common penalty functions. Simulation studies show good performance in identifying important markers with different penalty functions. In a motivating application involving a cohort of patients with psoriatic arthritis, we identify which, among a large group of candidate HLA markers, are associated with rapid disease progression.
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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) | 0.000 | 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