A Latent‐Class Model for Time‐To‐Event Outcomes and High‐Dimensional Imaging Data
Bibliographic record
Abstract
Structural magnetic resonance imaging (MRI) is one of the primary predictors of Alzheimer's disease risk, enabling the identification of patients with similar risk profiles for precision medicine treatment. Motivated by the need for flexible modeling in AD research, we propose a latent-class model that addresses the heterogeneity within study populations. This model allows for varying relationships between covariates and survival outcomes, accommodating the dynamics of AD progression. The imaging predictors are characterized by bivariate splines over triangulation to accommodate the irregular domain of the brain images. We develop a generalized expectation-maximization (EM) algorithm that combines the computational methods for logistic regression and penalized proportional hazards models to implement the proposed approach. We demonstrate the advantages of the proposed method through extensive simulation studies and provide an application to the Alzheimer's Disease Neuroimaging Initiative (ADNI) study, which helps to reveal different subtypes or stages of the disease process in Alzheimer's Disease.
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How this classification was reachedexpand
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 | 0.011 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".