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.

```
<- df2milist(lbpmilr, impvar = "Impnr")
imp_data
<- with(data=imp_data,
ra expr = cindex(glm(Chronic ~ Gender + Radiation, family=binomial)))
<- pool_cindex(ra)
res
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"
```