The geessbin function analyzes small-sample clustered or longitudinal data using modified generalized estimating equations (GEE) with bias-adjusted covariance estimator. This function assumes binary outcome and uses the logit link function. This function provides any combination of three GEE methods (conventional and two modified GEE methods) and 12 covariance estimators (unadjusted and 11 bias-adjusted estimators).
You can install the released version of geessbin from CRAN with:
install.packages("geessbin")And the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("rtishii/geessbin")This is a basic example which shows you how to solve a common problem:
library(geessbin)
data(wheeze)
# analysis of PGEE method with Morel et al. covariance estimator
res <- geessbin(formula = Wheeze ~ City + factor(Age), data = wheeze, id = ID,
corstr = "ar1", repeated = Age, beta.method = "PGEE",
SE.method = "MB")
print(res)
#> Call:
#> geessbin(formula = Wheeze ~ City + factor(Age), data = wheeze,
#> id = ID, corstr = "ar1", repeated = Age, beta.method = "PGEE",
#> SE.method = "MB")
#>
#> Correlation Structure: ar1
#> Estimation Method for Regression Coefficients: PGEE
#> Estimation Method for Standard Errors: MB
#>
#> Number of observations: 64
#> Number of clusters: 16
#> Maximum cluster size: 4
#>
#> Coefficients:
#> (Intercept) City factor(Age)10 factor(Age)11 factor(Age)12
#> -0.546 0.226 -0.237 -0.511 -0.525
#>
#> Estimated Scale Parameter: 1.04
#> Number of Iterations: 8
#>
#> Working Correlation:
#> 9 10 11 12
#> 9 1.0000 0.411 0.169 0.0693
#> 10 0.4107 1.000 0.411 0.1687
#> 11 0.1687 0.411 1.000 0.4107
#> 12 0.0693 0.169 0.411 1.0000
#>
#> Convergence status: Converged
# hypothesis tests for regression coefficients
summary(res)
#> Call:
#> geessbin(formula = Wheeze ~ City + factor(Age), data = wheeze,
#> id = ID, corstr = "ar1", repeated = Age, beta.method = "PGEE",
#> SE.method = "MB")
#>
#> Correlation Structure: ar1
#> Estimation Method for Regression Coefficients: PGEE
#> Estimation Method for Standard Errors: MB
#>
#> Coefficients:
#> Estimate Std.err Z Pr(>|Z|)
#> (Intercept) -0.546 0.782 -0.699 0.485
#> City 0.226 0.827 0.273 0.785
#> factor(Age)10 -0.237 0.752 -0.315 0.753
#> factor(Age)11 -0.511 0.870 -0.588 0.557
#> factor(Age)12 -0.525 0.986 -0.532 0.594
#>
#> Odds Ratios with 95% Confidence Intervals :
#> Odds Ratio Lower Limit Upper Limit
#> City 1.254 0.2477 6.35
#> factor(Age)10 0.789 0.1809 3.44
#> factor(Age)11 0.600 0.1090 3.30
#> factor(Age)12 0.592 0.0857 4.09
#>
#> Estimated Scale Parameter: 1.04
#> Number of Iterations: 8
#>
#> Working Correlation:
#> 9 10 11 12
#> 9 1.0000 0.411 0.169 0.0693
#> 10 0.4107 1.000 0.411 0.1687
#> 11 0.1687 0.411 1.000 0.4107
#> 12 0.0693 0.169 0.411 1.0000