Transition Models for Multivariate Longitudinal Binary Data
Why this work is in the frame
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Bibliographic record
Abstract
In many settings with longitudinal binary data, interest lies in modeling covariate effects on transition probabilities of an underlying stochastic process. When data from two or more processes are available, the scientific focus may be on the degree to which changes in one process are associated with changes in another process. Analysis based on independent Markov models permits separate examination of covariate effects on the transition probabilities for each process, but no insight into between-process associations is obtained. We propose a method of estimation and inference based on joint transitional models for multivariate longitudinal binary data using GEE2 or alternating logistic regression that allows modeling of covariate effects on marginal transition probabilities as well as the association parameters. Consistent estimates of regression coefficients and association parameters are obtained, and efficiency gains for the parameters governing the marginal transition probabilities are realized when the association between processes is strong. Extensions to deal with multivariate longitudinal categorical data are indicated.
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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 | 0.004 | 0.009 |
| 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