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.
| Version: |
0.1.1 |
| Depends: |
R (≥ 4.0.0) |
| Imports: |
nlme, stats, graphics, utils |
| Suggests: |
testthat (≥ 3.0.0), knitr, rmarkdown, shiny |
| Published: |
2026-09-17 |
| DOI: |
10.32614/CRAN.package.iPEB (may not be active yet) |
| Author: |
Bitan Sarkar 
[aut, cre, cph],
Ana Maria Kenney [aut],
James P. Long [aut],
Johannes F. Fahrmann [aut],
Samir Hanash [aut],
Kim-Anh Do [aut],
Ehsan Irajizad [aut] |
| Maintainer: |
Bitan Sarkar <bitansarkar010899 at gmail.com> |
| BugReports: |
https://github.com/bitansa/iPEB/issues |
| License: |
MIT + file LICENSE |
| URL: |
https://github.com/bitansa/iPEB |
| NeedsCompilation: |
no |
| Materials: |
README, NEWS |
| CRAN checks: |
iPEB results |
Documentation:
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