Joint analysis of a quantile of longitudinal \noutcomes and multiple time to events with \ncensoring
Bibliographic record
Abstract
It is very common in health science studies that we observe both longitudinal and survival \ndata, within which different types of data are correlated and need to be analysed \ntogether to draw accurate conclusions. In this thesis, we propose a new method to \njointly analyse observations of a longitudinal outcome and occurring times for multiple \nright- and interval-censored events to capture the underlying effects between them. \nIn order to have a more complete view, we apply the quantile regression techniques \nto measure the effects of covariates on the longitudinal observations and then the effects \nof longitudinal observations on the occurring times of events at different levels of \nquantile. Semi-parametric proportional hazards models are proposed for both right and \ninterval-censored events with a vector of possible time-varying covariates shared \nwith the quantile regression model for the longitudinal outcome. We also assume a \nvariable of random effects in the survival models to measure the dependence between \ndifferent events. We develop a Monte Carlo Expectation Maximization (MCEM) algorithm \nfor computing non-parametric maximum likelihood estimators of parameters. \nOur estimators are proved to be consistent and asymptotically normally distributed. \nFurthermore, our proposed joint model is illustrated through a series of extensive simulation \nstudies and an application to a data set from a French cohort study, PAQUID, \naiming at studying the cognitive decline, such as the disease of dementia, among the \nelderly.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".