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CIMPLE: Analysis of Longitudinal Electronic Health Record (EHR) Data
with Possibly Informative Observational Time
Analyzes longitudinal Electronic Health Record (EHR) data with possibly informative observational time. These methods are grouped into two classes depending on the inferential task. One group focuses on estimating the effect of an exposure on a longitudinal biomarker while the other group assesses the impact of a longitudinal biomarker on time-to-diagnosis outcomes. The accompanying paper is Du et al (2024) <doi:10.48550/arXiv.2410.13113>.
| Version: |
0.1.0 |
| Depends: |
R (≥ 2.10) |
| Imports: |
dplyr, JMbayes2, lme4, mice, nleqslv, nlme, statmod, stats, survival, utils |
| Published: |
2024-11-12 |
| DOI: |
10.32614/CRAN.package.CIMPLE |
| Author: |
Jiacong Du  [aut],
Howard Baik [cre] |
| Maintainer: |
Howard Baik <howard.baik at yale.edu> |
| License: |
GPL (≥ 3) |
| NeedsCompilation: |
no |
| Materials: |
README NEWS |
| CRAN checks: |
CIMPLE results |
Documentation:
Downloads:
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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