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

ABSTRACT:

Background

The generalized estimating equation (GEE) method is widely used for analyzing longitudinal and clustered data. Although the GEE estimate for regression coefficients and sandwich covariance estimate are consistent regardless of the choice of covariance structure, they are generally biased for small sample sizes. Various researchers have proposed modified GEE methods and covariance estimators to handle small-sample bias.

Results

We briefly present bias-corrected and penalized GEE methods, along with 11 bias-adjusted covariance estimators. In addition, we focus on analyzing longitudinal or clustered data with binary outcomes using the logit link function and introduce package geessbin in R to implement conventional and modified GEE methods with bias-adjusted covarian

SUBMITTER: Ishii R 

PROVIDER: S-EPMC11558877 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature