A New Logistic Model with Subject‐Specific and Serially Correlated Time‐Specific Distribution‐Free Random Effects on the Unit Interval for Longitudinal Binary Data
Bibliographic record
Abstract
Various beta-binomial mixed effects models have been developed in recent years for longitudinal binary data; however, these approaches rely heavily on the parametric specification of beta and normal random effects. Furthermore, their incorporation of normal random effects into beta-binomial models has been done at the sacrifice of certain computational convenience and clear interpretation with beta-binomial models. In this paper, we introduce a new model that incorporates subject-specific and serially correlated time-specific distribution-free random effects on the unit interval into logistic regression multiplicatively with fixed effects. This new multiplicative model facilitates the interpretation of random effects on the unit interval as risk modifiers. This multiplicative model setup also eases the model derivation and random effects prediction. A quasi-likelihood approach has been developed in the estimation of our model. Our results are robust against random effects distributions. Our method is illustrated through the analysis of multiple sclerosis trial data.
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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 | 0.013 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".