Hypothesis testing in joint models for longitudinal and time-to-event outcomes
Bibliographic record
Abstract
Many clinical studies collect longitudinal biomarkers known to be highly associated with a time-to-event outcome. Motivated by the problem of testing genetic association in this setting, we investigate joint models for the association of genetic variants with longitudinal measurements and time to event. We develop and validate a closed-form sample size formula for an overall genotype association in this setting, and conduct simulations to compare joint model approaches to test for direct/indirect/overall genotype associations with time to event. To improve robustness to model misspecification due to non-linearity of the longitudinal traits, we make use of spline functions to capture nonlinear subject-specific evolutions in the longitudinal process. In the simulation study, we also assess the sensitivity of inference to misspecification, and evaluate the validity and power of hypothesis tests in joint modelling. Different joint modelling approaches are implemented in an application to genetic data from the Diabetes Control and Complications Trial.
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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 | 0.255 | 0.422 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.011 | 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".