Semiparametric marginal methods for clustered data adjusting for informative cluster size with nonignorable zeros-Reference-Cited by-同舟云学术

Semiparametric marginal methods for clustered data adjusting for informative cluster size with nonignorable zeros

Published:2022-03-08 Issue:5 Volume:64 Page:898-911
ISSN:0323-3847
Container-title:Biometrical Journal
language:en
Short-container-title:Biometrical J

Author:

Shen Biyi¹^ORCID,Chen Chixiang²^ORCID,Chinchilli Vernon M.¹,Ghahramani Nasrollah³,Zhang Lijun⁴,Wang Ming¹^ORCID

Affiliation:

1. Division of Biostatistics and Bioinformatics Department of Public Health Sciences Penn State College of Medicine Hershey PA USA

2. Division of Biostatistics and Bioinformatics Department of Epidemiology and Public Health University of Maryland School of Medicine Baltimore MD USA

3. Division of Nephrology Penn State College of Medicine Hershey PA USA

4. Institute of Personalized Medicine Penn State College of Medicine Hershey PA USA

Funder

Pennsylvania Department of Health

National Center for Advancing Translational Sciences

National Institute of Diabetes and Digestive and Kidney Diseases

Publisher

Wiley

Subject

Statistics, Probability and Uncertainty,General Medicine,Statistics and Probability

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/bimj.202100161

Reference26 articles.

1. Mean estimating equation approach to analysing cluster-correlated data with nonignorable cluster sizes

2. Cluster adjusted regression for displaced subject data (CARDS): Marginal inference under potentially informative temporal cluster size profiles

3. Efficient estimation methods for informative cluster size data;Chiang C. T.;Statistica Sinica,2008

4. A Bayesian Approach for Joint Modeling of Cluster Size and Subunit-Specific Outcomes

5. Within-cluster resampling

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