A threshold mixed-effects Tobit model for treatment-sensitive subgroup identification based on longitudinal measures with floor and ceiling effects and a continuous covariate
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
In the era of personalized medicine, there is an increasing interest in the identification of patients who may benefit from or be sensitive to a specific type of treatment. Recently a threshold linear mixed model was proposed to identify treatment-sensitive subgroups based on a continuous covariate when longitudinal measurements are the outcomes of the study. This model assumes, however, a normal distribution for these measurements. In some studies, the longitudinal measurements are restricted in an interval and subject to floor and ceiling effects caused by a portion of subjects with measurements on the boundaries of the interval, which would violate the normality assumption. In this paper, a threshold mixed-effects Tobit model is introduced to overcome this problem. The proposed models and inference procedures are assessed through simulation studies, as well as an application to the analysis of data from a randomized clinical trial.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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