Statistical analysis of repeated measurements with informative censoring times
Why this work is in the frame
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Bibliographic record
Abstract
Incomplete repeated measurement data often arise in medical studies. A problem that has recently drawn much attention in the literature in this situation is that the incompleteness or missingness is informative or related to the underlying variable of interest. In this paper we propose a non-parametric global test for treatment comparison in the presence of informative incompleteness. A semi-parametric regression model is also presented for assessing conditional treatment effects given the drop-out patterns, adopting the idea similar to that behind the pattern-mixture modelling approach and discussed in Shih and Quan. The proposed methods can be easily implemented and are conceptually simple and similar too, but can be applied to more general cases than those given in Yao et al. They are evaluated by numerical studies and applied to data from a clinical trial of adult schizophrenics.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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