The miceafter package includes the function
pool_cindex, to pool c-index values from logistic and Cox
regression models. This vignette shows you how to use this function.
mice function and
Logistic RegressionThe lbp_orig is a dataset as part of the miceafter package with
missing values. So we first impute them with the mice
function. Than we use the mids2milist function to turn the
mids object with multiply imputed datasets, as a result of
using mice, into a milist object. Than we use
the with function to apply repeated analyses with the
cindex function across the multiply imputed datasets.
Finally, we pool the results by using the pool_cindex
function. We do that in one pipe.
lbp_orig %>%
mice(m=5, seed=3025, printFlag = FALSE) %>%
mids2milist() %>%
with(expr = cindex(glm(Chronic ~ Gender + Radiation, family=binomial))) %>%
pool_cindex()
#> C-index Critical value 95 CI low 95 CI high
#> [1,] 0.6553774 1.97818 0.567203 0.734012
#> attr(,"class")
#> [1] "mipool"The dataset lbpmilr as part of the miceafter package is
a long dataset that contains 10 multiply imputed datasets. The datasets
are distinguished by the Impnr variable. First we convert
the dataset into a milist object by using the
df2milist function. Than we use the with
function to apply repeated analyses with the cindex
function across the multiply imputed datasets. Finally, we pool the
results by using the pool_cindex function.
imp_data <- df2milist(lbpmilr, impvar = "Impnr")
ra <- with(data=imp_data,
expr = cindex(glm(Chronic ~ Gender + Radiation, family=binomial)))
res <- pool_cindex(ra)
res
#> C-index Critical value 95 CI low 95 CI high
#> [1,] 0.6638267 1.976656 0.5764274 0.7412861
#> attr(,"class")
#> [1] "mipool"The dataset lbpmicox as part of the miceafter package is
a long dataset that contains 10 multiply imputed datasets. The datasets
are distinguished by the Impnr variable. First we convert
the dataset into a milist object by using the
df2milist function. Than we use the with
function to apply repeated analyses with the cindex
function across the list of multiply imputed datasets. Finally, we pool
the results by using the pool_cindex function.
library(survival)
lbpmicox %>%
df2milist(impvar = "Impnr") %>%
with(expr = cindex(coxph(Surv(Time, Status) ~ Radiation + Age))) %>%
pool_cindex()
#> C-index Critical value 95 CI low 95 CI high
#> [1,] 0.5413464 1.959964 0.4952103 0.5867842
#> attr(,"class")
#> [1] "mipool"