Marginal clustered multistate models for longitudinal progressive processes with informative cluster size
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
Abstract Informative cluster size (ICS) is a phenomenon where cluster size is related to the outcome. While multistate models can be applied to characterize the unit‐level transition process for clustered interval‐censored data, there is a research gap addressing ICS within this framework. We propose two extensions of multistate model that account for ICS to make marginal inference: one by incorporating within‐cluster resampling and another by constructing cluster‐weighted score functions. We evaluate the performances of the proposed methods through simulation studies and apply them to the Veterans Affairs Dental Longitudinal Study (VADLS) to understand the effect of risk factors on periodontal disease progression. ICS occurs frequently in dental data, particularly in the study of periodontal disease, as people with fewer teeth due to the disease are more susceptible to disease progression. According to the simulation results, the mean estimates of the parameters obtained from the proposed methods are close to the true values, but methods that ignore ICS can lead to substantial bias. Our proposed methods for clustered multistate model are able to appropriately take ICS into account when making marginal inference of a typical unit from a randomly sampled cluster.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 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