geessbin: an R package for analyzing small-sample binary data using modified generalized estimating equations with bias-adjusted covariance estimators

The geessbin package provides three GEE estimates with numerous covariance estimates. It is useful for analyzing correlated data such as longitudinal and clustered data. Additionally, the geessbin is designed to be user-friendly, making it accessible to non-statisticians.

Authors

Kazushi Maruo (ORCID: https://orcid.org/0000-0002-3093-0386)
Ryota Ishii (ORCID: https://orcid.org/0000-0003-1725-4579)
Masahiko Gosho (ORCID: https://orcid.org/0000-0002-5973-9163)
Tomohiro Ohigashi (ORCID: https://orcid.org/0000-0002-9630-5882)

Institutions

University of Tsukuba (JP)
Tokyo University of Science (JP)

Publication Details

Journal: BMC Medical Research Methodology
Published: 2024-11-13
DOI: https://doi.org/10.1186/s12874-024-02368-2
Citations: 4
Primary Topic: Statistical Methods and Bayesian Inference
Type: article
Field-Weighted Citation Impact: 3.55

Funders

Japan Society for the Promotion of Science

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article

geessbin: an R package for analyzing small-sample binary data using modified generalized estimating equations with bias-adjusted covariance estimators

Kazushi Maruo, Ryota Ishii, Masahiko Gosho, Tomohiro Ohigashi

Nov 13, 2024

4 citations

BMC Medical Research Methodology

Statistical Methods and Bayesian Inference

3.55

article

geessbin: an R package for analyzing small-sample binary data using modified generalized estimating equations with bias-adjusted covariance estimators

Kazushi Maruo, Ryota Ishii, Masahiko Gosho, Tomohiro Ohigashi

November 13, 2024article en

4 citations

Abstract

The geessbin package provides three GEE estimates with numerous covariance estimates. It is useful for analyzing correlated data such as longitudinal and clustered data. Additionally, the geessbin is designed to be user-friendly, making it accessible to non-statisticians.

BMC Medical Research MethodologyVol. 24(1)

University of Tsukuba (JP), Tokyo University of Science (JP)

Japan Society for the Promotion of Science

Openalex Percentile: Top 7%

Statistical Methods and Bayesian Inference

3.55

DOI Open Access PDF OpenAlex

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geessbin: an R package for analyzing small-sample binary data using modified generalized estimating equations with bias-adjusted covariance estimators — Kazushi Maruo, Ryota Ishii, et al. · BMC Medical Research Methodology (2024) | TGRS Research Map | TGRS