Bayesian Analysis of Transition Model for Longitudinal Ordinal Response Data: Application to Insomnia Data
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, we present a Bayesian framework for analyzing longitudinal ordinal response data. In analyzing longitudinal data, the possibility of correlations between responses given by the same individual needs to be taken into account. Various models can be used to handle such correlations such as marginal modeling, random effect modeling and transition (Markov) modeling. Here a transition modeling is used and a Bayesian approach is presented for analyzing longitudinal data. A cumulative logistic regression model and the Bayesian method, using MCMC, are implemented for obtaining the parameters estimates. Our approach is applied on a two-period longitudinal Insomnia data where the Bayesian estimate for measure of association, , between the initial and follow-up ordinal responses is obtained in each level of a treatment variable. Then, the sensitivity of posterior summaries to changes of prior hyperparameters is investigated. We also use Bayes factor criterion for testing some important hypotheses
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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 | 0.023 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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