A composite semiparametric homogeneity test for the distributions of multigroup interval-bounded longitudinal data
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Bibliographic record
Abstract
Motivated by comparing the distribution of longitudinal quality of life (QoL) data among different treatment groups from a cancer clinical trial, we propose a semiparametric test statistic for the homogeneity of the distributions of multigroup longitudinal measurements, which are bounded in a closed interval with excess observations taking the boundary values. Our procedure is based on a three-component mixed density ratio model and a composite empirical likelihood for the longitudinal data taking values inside the interval. A nonparametric bootstrap method is applied to calculate the p-value of the proposed test. Simulation studies are conducted to evaluate the proposed procedure, which show that the proposed test is effective in controlling type I errors and more powerful than the procedure which ignores the values on the boundaries. It is also robust to the model mispecification than the parametric test. The proposed procedure is also applied to compare the distributions of the scores of Physical Function subscale and Global Heath Status between the patients randomized to two treatment groups in a cancer clinical trial.
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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 | 0.028 |
| 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 | 0.001 | 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