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Joint modeling of longitudinal data with informative cluster size adjusted for zero-inflation and a dependent terminal event
Biyi Shen
, Chixiang Chen
, Danping Liu
, Somnath Datta
,
Nasrollah Ghahramani
,
Vernon M. Chinchilli
, Ming Wang
Department of Medicine
Division of Nephrology
Department of Public Health Sciences
Division of Biostatistics and Bioinformatics
Penn State Clinical and Translational Science Institute (CTSI)
Penn State Cancer Institute
Cancer Institute, Cancer Control
Research output
:
Contribution to journal
›
Article
›
peer-review
3
Scopus citations
Overview
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Keyphrases
Longitudinal Data
100%
Zero-inflation
100%
Joint Modeling
100%
Informative Cluster Size
100%
Terminal Event
100%
Acute Kidney Injury
66%
Clinical Trials
33%
Modeling Approach
33%
Parameter Estimation
33%
Health Condition
33%
Longitudinal Follow-up
33%
Serum Creatinine
33%
Specific Events
33%
Overall Health
33%
Data Needs
33%
Longitudinal Measures
33%
Serial Assessment
33%
Parameter Inference
33%
Recurrent Events
33%
Likelihood-based Approach
33%
Zero-inflated
33%
Clinical Observational Study
33%
Mathematics
Longitudinal Data
100%
Joint Modeling
100%
Parameter Estimation
50%
Modeling Approach
50%
Serum Creatinine
50%