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

Abstract

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 covariance estimators. Finally, we illustrate the implementation and detail a usage example of the package. The package is available from the Comprehensive R Archive Network (CRAN) at https://cran.r-project.org/web/packages/geessbin/index.html . Conclusions 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.

Keywords

• Bias correction
• Sandwich covariane estimator
• Small-sample size