Joint modeling of clustered binary data with crossed random effects via the Gaussian copula mixed model
Bibliographic record
Abstract
Models with crossed random effects are common in reader-based diagnostic studies, where the same group of readers evaluate patients for certain diseases; an example is diabetic retinopathy study in Alberta, Canada. Although generalized linear mixed models (GLMMs) are well developed for non-Gaussian responses (e.g., binary outcomes) with crossed random effects, evaluation of the marginal likelihood is still technically and computationally demanding and can become prohibitive in applications, since the data cannot be grouped into independent blocks. The available estimation methods are also not free from problems. A recent approach involves application of data cloning (DC) to obtain maximum likelihood (ML) estimates using a Bayesian framework. Their approach is proved to be superior over the other two alternatives they considered in terms of providing relatively unbiased and efficient parameter estimates. However, this approach is based on a multivariate latent Gaussian description of the multiple correlated binary outcomes. In this thesis, we relax this assumption by allowing for disparate non-Gaussian latent variables for the binary responses, and propose a joint modeling via the Gaussian copula mixed model (GCMM). We applied maximum pairwise likelihood (PL) estimation instead of doing full ML analysis to reduce computational complexities. We conducted simulation studies with a setting analogous to the diabetic retinopathy data to see the performance of PL estimators for GCMM with crossed random effects. Simulation results suggest that although the estimation of regression coefficients and correlation parameter exhibit no problem, a much bigger sample size is required for the other scale parameters to provide reasonably accurate approximate results. We also analyzed the retinopathy data with the proposed approach considering three different conditional margins.
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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 | 0.016 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".