Joint Survival Analysis of Time to Drug Change and a Terminal Event with Application to Drug Failure Analysis using Transplant Registry Data
Bibliographic record
Abstract
Statistical approaches for drug effectiveness studies after liver transplant have used a survival model with changes in treatment as a time-dependent covariate. However, the approach requires that changes in the time-dependent covariate be unrelated to survival outcome. Usually this is not the case, as one drug may be discontinued and an alternative chosen due to the declining health status of the patient. Other approaches examine only subjects who remain on the same drug over a time window, which discards valuable data and may lead to biased effects since this excludes data related to early deaths and to individuals who perform poorly on the drug and had to switch treatments. Because of these issues there are conflicting results seen in the evaluation of immunosuppressive drug effectiveness after liver transplant. We propose a joint survival outcome model with a time-to-drug-change event and a terminal event in graft failure that is useful in drug effectiveness studies where subjects are discontinued from an immunosuppressant (in favour of alternative treatment) due to health reasons. We also include a longitudinal biomarker component. The model takes account of the dependencies across out- comes through shared random effects. Using a Markov chain Monte Carlo approach, we fit the joint model to data from liver transplant recipients from the Scientific Registry for Transplant Recipients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.049 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".