iPEB
Improved Parametric Empirical Bayes for Longitudinal Biomarker Analysis
Extends parametric empirical Bayes (PEB) for longitudinal biomarker screening with a time-gap-aware standardization layer, covariate adjustment, and objective-driven multi-marker weighting. The layer models each subject's biomarker history with a random intercept (and an optional random slope) and autocorrelated, gap-scaled residuals, so that prediction uncertainty grows with the time between visits and per-visit specificity is preserved under irregular sampling. Marker weights are learned to optimize a user-selected clinical objective -- maximizing sensitivity at a fixed specificity, extending detection lead time, or a combined objective -- with optional feature selection and a choice of scalar or multivariate combiner. Functions for fitting, prediction, and evaluation (sensitivity, lead time, and specificity at chosen operating points) are provided. A manuscript describing the method is in preparation.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.1.1 |
rolling linux/jammy R-4.5 | iPEB_0.1.1.tar.gz |
144.4 KiB |
0.1.1 |
rolling linux/noble R-4.5 | iPEB_0.1.1.tar.gz |
144.2 KiB |
0.1.1 |
rolling source/ R- | iPEB_0.1.1.tar.gz |
72.9 KiB |
0.1.1 |
latest linux/jammy R-4.5 | iPEB_0.1.1.tar.gz |
144.4 KiB |
0.1.1 |
latest linux/noble R-4.5 | iPEB_0.1.1.tar.gz |
144.2 KiB |
0.1.1 |
latest source/ R- | iPEB_0.1.1.tar.gz |
72.9 KiB |
0.1.1 |
2026-04-23 source/ R- | iPEB_0.1.1.tar.gz |
0 B |