Getting the OR with confidence intervals using the GEE (sandwhich) standard errors
set.seed(100)
library(mets)
data(bmt);
bmt$id <- sample(1:100,408,replace=TRUE)
glm1 <- glm(tcell~platelet+age,bmt,family=binomial)
summaryGLM(glm1)
#> $coef
#> Estimate Std.Err 2.5% 97.5% P-value
#> (Intercept) -2.4371 0.2225 -2.8732 -2.0009 6.481e-28
#> platelet 1.1368 0.3076 0.5340 1.7397 2.189e-04
#> age 0.5927 0.1551 0.2888 0.8966 1.319e-04
#>
#> $or
#> Estimate 2.5% 97.5%
#> (Intercept) 0.08741654 0.05651794 0.1352076
#> platelet 3.11688928 1.70573194 5.6955015
#> age 1.80895115 1.33489115 2.4513641
#>
#> $fout
#> NULL
## GEE robust standard errors
summaryGLM(glm1,id=bmt$id)
#> $coef
#> Estimate Std.Err 2.5% 97.5% P-value
#> (Intercept) -2.4371 0.2157 -2.8599 -2.0142 1.361e-29
#> platelet 1.1368 0.2830 0.5822 1.6914 5.877e-05
#> age 0.5927 0.1434 0.3117 0.8738 3.568e-05
#>
#> $or
#> Estimate 2.5% 97.5%
#> (Intercept) 0.08741654 0.05727471 0.1334211
#> platelet 3.11688928 1.79006045 5.4271903
#> age 1.80895115 1.36575550 2.3959664
#>
#> $fout
#> NULL
Predictions also simple
age <- seq(-2,2,by=0.1)
nd <- data.frame(platelet=0,age=seq(-2,2,by=0.1))
pnd <- predictGLM(glm1,nd)
head(pnd$pred)
#> Estimate 2.5% 97.5%
#> p1 0.02601899 0.01115243 0.05951051
#> p2 0.02756409 0.01214068 0.06136414
#> p3 0.02919819 0.01321187 0.06328733
#> p4 0.03092608 0.01437206 0.06528441
#> p5 0.03275278 0.01562757 0.06736019
#> p6 0.03468351 0.01698493 0.06952008
plot(age,pnd$pred[,1],type="l",ylab="predictions",xlab="age",ylim=c(0,0.3))
matlines(age,pnd$pred[,-1],col=2)
sessionInfo()
#> R version 4.4.2 (2024-10-31)
#> Platform: aarch64-apple-darwin24.2.0
#> Running under: macOS Sequoia 15.2
#>
#> Matrix products: default
#> BLAS: /Users/klaus/.asdf/installs/R/4.4.2/lib/R/lib/libRblas.dylib
#> LAPACK: /Users/klaus/.asdf/installs/R/4.4.2/lib/R/lib/libRlapack.dylib; LAPACK version 3.12.0
#>
#> locale:
#> [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
#>
#> time zone: Europe/Copenhagen
#> tzcode source: internal
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] mets_1.3.5 timereg_2.0.6 survival_3.8-3
#>
#> loaded via a namespace (and not attached):
#> [1] cli_3.6.3 knitr_1.49 rlang_1.1.4
#> [4] xfun_0.50 jsonlite_1.8.9 listenv_0.9.1
#> [7] future.apply_1.11.3 lava_1.8.1 htmltools_0.5.8.1
#> [10] sass_0.4.9 rmarkdown_2.29 grid_4.4.2
#> [13] evaluate_1.0.1 jquerylib_0.1.4 fastmap_1.2.0
#> [16] mvtnorm_1.3-2 yaml_2.3.10 lifecycle_1.0.4
#> [19] numDeriv_2016.8-1.1 compiler_4.4.2 codetools_0.2-20
#> [22] ucminf_1.2.2 Rcpp_1.0.13-1 future_1.34.0
#> [25] lattice_0.22-6 digest_0.6.37 R6_2.5.1
#> [28] parallelly_1.41.0 parallel_4.4.2 splines_4.4.2
#> [31] bslib_0.8.0 Matrix_1.7-1 tools_4.4.2
#> [34] globals_0.16.3 cachem_1.1.0