TheRPackagegeepackfor Generalized Estimating Equations

Abstract

This paper describes the core features of the R package geepack, which implements the generalized estimating equations (GEE) approach for fitting marginal generalized linear models to clustered data. Clustered data arise in many applications such as longitudinal data and repeated measures. The GEE approach focuses on models for the mean of the correlated observations within clusters without fully specifying the joint distribution of the observations. It has been widely used in statistical practice. This paper illustrates the application of the GEE approach with geepack through an example of clustered binary data.

Special education students’ use of indirect addition in solving subtraction problems up to 100—A proof of the didactical potential of an ignored procedure

“…We did this by making use of generalized estimating equations (GEE). In this approach, standard errors of regression coefficients are adjusted as a result of the cross-classified data structure (Halekoh, Højsgaard, & Yan, 2006).…”

The statistical analysis of crash-frequency data: A review and assessment of methodological alternatives

“…The generalized estimating equation model offers different approaches to handle serial correlation including independence, exchangeable, dependence, and autoregressive type 1 correlation structures. Usually, the correlation structure in the estimation process has a minimal influence on the modeling output when count-data models are used with a complete dataset (few if any omitted variables), however, the selection of the correlation type can be critical when the database has omitted variables Halekoh et al, 2006;Lord and Mahlawat, 2009). …”

Competing regression models for longitudinal data

“…Other estimation methods are available, for example, in Fitzmaurice et al (2008). (Wolfinger and O'Connell, 1993) GEE GENMOD geepack GEE (Halekoh et al, 2006) Finally, we mention that because of the relatively small (but not uncommon in studies of this type) sample size (32 children with four observations/child), the conclusions based on asymptotic results should be viewed with caution. A good practice in such cases is to fit different models to the data, use different diagnostic tools to detect possible outliers, and verify whether the results are coherent.…”

“…This model was fitted via the R package geepack (Halekoh et al, 2006) using GEE that allows for different covariates and even different link functions and estimating equations for the mean, scale, and correlations as considered in Yan and Fine (2004). The estimates of β * jd and of the other parameters of model (11) and (12) are presented in Table 2.…”