Statistical methods for multivariate interval‐censored recurrent events
Bibliographic record
Abstract
Multi-type recurrent event data arise when two or more different kinds of events may occur repeatedly over a period of observation. The scientific objectives in such settings are often to describe features of the marginal processes and to study the association between the different types of events. Interval-censored multi-type recurrent event data arise when the precise event times are unobserved, but intervals are available during which the events are known to have occurred. This type of data is common in studies of patients with advanced cancer, for example, where the events may represent the development of different types of metastatic lesions which are only detectable by conducting bone scans of the entire skeleton. In this setting it is of interest to characterize the incidence of the various types of bone lesions, to estimate the impact of treatment and other covariate effects on the development of new lesions, and to understand the relationship between the processes generating the bone lesions. We develop joint models for multi-type interval-censored recurrent events which accommodate dependencies between different types of events and enable one to examine the covariate effects via regression. However, since the marginal likelihood resulting from the multivariate random effect model is intractable, we describe a Gibbs sampling algorithm to facilitate model fitting and inference. We use generalized estimating equations for estimation and inference based on marginal models. The finite sample properties of the marginal approach are studied via simulation. The estimates of both the regression coefficients and the variance-covariance parameters are shown to have negligible bias and 95 per cent confidence intervals based on the asymptotic variance formula are shown to have excellent empirical coverage probabilities in all of the settings considered. The application of these methods to data from a trial of women with advanced breast cancer provides insight into the clinical course of bone metastases in this population.
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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 | 0.038 | 0.140 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.002 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".