Estimation methods for marginal and association parameters for longitudinal binary data with nonignorable missing observations
Bibliographic record
Abstract
In longitudinal studies, missing observations occur commonly. It has been well known that biased results could be produced if missingness is not properly handled in the analysis. Authors have developed many methods with the focus on either incomplete response or missing covariate observations, but rarely on both. The complexity of modeling and computational difficulty would be the major challenges in handling missingness in both response and covariate variables. In this paper, we develop methods using the pairwise likelihood formulation to handle longitudinal binary data with missing observations present in both response and covariate variables. We propose a unified framework to accommodate various types of missing data patterns. We evaluate the performance of the methods empirically under a variety of circumstances. In particular, we investigate issues on efficiency and robustness. We analyze longitudinal data from the National Population Health Study with the use of our methods.
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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 | 0.040 | 0.135 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".