Marginal Methods for Incomplete Longitudinal Data Arising in Clusters
Why this work is in the frame
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Bibliographic record
Abstract
Inverse probability–weighted generalized estimating equations are commonly used to deal with incomplete longitudinal data arising from a missing-at-random mechanism when the marginal means are of primary interest. In many cases, however, the repeated measurements themselves may arise in clusters, which leads to both a cross-sectional and a longitudinal correlation structure. In some applications, the degree of these types of correlation may become of scientific interest. Here we develop inverse probability–weighted second-order estimating equations for monotone missing-data patterns which, under specified assumptions, facilitate consistent estimation of the marginal mean parameters and association parameters. Here the missing-data model accommodates cross-sectional clustering in the missing-data indicators, and the probabilities are estimated under a multivariate Plackett model. For computational reasons, we also consider using the alternating logistic regression algorithm for estimation of the association parameters for the responses. We investigate the importance of modeling the cross-sectional clustering in the missing-data process by simulation. An extension to deal with intermittently missing data is provided, and an application to a longitudinal cluster-randomized smoking prevention trial is presented.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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