CIMEHR
0.1.0Gaussian Clinically Informative Visiting and Observation Processes in Electronic Health Record (EHR) Data
Overview
Fits semiparametric joint models for longitudinal electronic health record (EHR) data that addresses two-stage hierarchical missingness mechanism. The first stage is the visiting process, and the second stage is the observation process. The core CIMEHR method (Clinical Informative Missingness for Electronic Health Records) uses a three-stage procedure: partial likelihood with log-normal frailty for visit intensity, probit regression with shared latent factor-linked random effects for observation, and weighted least squares with risk-set centering for the outcome. These three stages are connected through a shared latent factor that induces dependence across all three processes. A data simulator and implementations of common benchmark methods (linear mixed models, multiple imputation, and others) are included for comparative studies. Detailed methods are described in Yang, Shi, and Mukherjee (2026) doi:10.48550/arXiv.2602.15374.
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Health
- OK2026-08-0513 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- NOTE2026-08-0112 OK · 1 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- OK2026-06-097 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
Documentation
- Examples that run
- 100%
- Documented parameters
- 98%
- Return-value docs
- 100%
- References docs
- 24%
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Checks run against github.com/ysph-dsde/cimehr on 2026-08-16.
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Code & Tests
Datasets
People & History
1 release. R releases are shown for context.
- 0.1.0Latest2026-06-08 · current release
- RR 4.6.0 released · 2026-04-24
Package metadata
- First published
- 2026-06-08
- Total releases
- 1 / 1 yrs
- License
- MIT + file LICENSE OSI
- Minimum R
- ≥ 3.5
- Bundled data
- 270 KB / 1 file
- Download size
- 779 KB
- Installed size
- not tracked yet
- With dependencies
- not tracked yet
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