Robust Inference for Incomplete Binary Longitudinal Data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Missing data occur in many longitudinal studies. When data are nonignorably missing, it is necessary to incorporate the missing data mechanism into the observed data likelihood function. A full likelihood analysis of nonignorable missing data is complicated algebraically, and often requires intensive computation, especially when there are many follow-up times. To avoid such computational difficulties, pseudo-likelihood methods have been proposed in the literature under minimal parametric assumptions. However, like the classical maximum likelihood estimators, these pseudo-likelihood estimators are also sensitive to potential outliers in the data. In this article, we propose and explore a robust method in the framework of a pseudo-likelihood function that is derived under the working assumption that the longitudinal responses are independent over time. The performance of the proposed robust method is investigated in simulations. The method is also illustrated in an example using actual data on CD4 counts from clinical trials of HIV-infected patients.
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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 | 0.002 | 0.001 |
| 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 | 0.001 |
| 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