Varying-Coefficient Marginal Models and Applications in Longitudinal Data Analysis
Bibliographic record
Abstract
We consider a class of nonparametric marginal models in which the regres-sion coefficients are assumed to be time-varying smooth functions. Such models are appealing in longitudinal data analysis to characterize the time-dependent effects of covariates on the expected value of the response vari-able. A local quasi-likelihood method is employed to estimate the coefficient functions, based on the nonparametric technique of local polynomial kernel regression. We establish the asymptotic distribution theory for the estima-tors considered. We conduct Monte Carlo simulation studies to compare two types of kernel-based GEE methods with global and local variance struc-tures, respectively. We illustrate the proposed models via three real-world data sets from a clinical trial of multiple sclerosis, a quality of life study in chemotherapeutic treatments on breast cancer, and a genomic fine-scale mapping association study on chromosomal region 5q31 for Crohn’s disease. AMS (2000) subject classification. Primary 62G08, 62J12, 62P10.
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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 | 0.035 | 0.114 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".