With the `miceafter`

package you can apply statistical and pooled analyses after multiple imputation. Therefore the name ‘miceafter’. The package contains a variety of statistical tests like the `pool_levenetest`

function to pool Levene’s tests across multiply imputed datasets or the `pool_propdiff_nw function`

to pool the difference between proportions according to method Newcombe-Wilson. The package also contains a function `pool_glm`

to pool and select linear and logistic regression functions. Functions can also be used in combination with the `%>%`

(Pipe) operator.

More and more statistical analyses and pooling functions will be added over time to form a framework of statistical tests that can be applied and pooled across multiply imputed datasets.

This example shows you how to pool the Levene test across 5 multiply imputed datasets. The pooling method that is used is method D1.

```
library(miceafter)
# Step 1: Turn data frame with multiply imputed datasets into object of 'milist'
imp_dat <- df2milist(lbpmilr, impvar="Impnr")
# Step 2: Do repeated analyses across multiply imputed datasets
ra <- with(imp_dat, expr=levene_test(Pain ~ factor(Carrying)))
# Step 3: Pool repeated test results
res <- pool_levenetest(ra, method="D1")
res
#> F_value df1 df2 P(>F) RIV
#> [1,] 1.586703 2 115.3418 0.209032 0.1809493
#> attr(,"class")
#> [1] "mipool"
```

```
library(miceafter)
# Step 1: Turn data frame with multiply imputed datasets into object of 'milist'
imp_dat <- df2milist(lbpmilr, impvar="Impnr")
# Step 2: Do repeated analyses across multiply imputed datasets
ra <- with(imp_dat,
expr=propdiff_wald(Chronic ~ Radiation, strata = TRUE))
# Step 3: Pool repeated test results
res <- pool_propdiff_nw(ra)
res
#> Prop diff CI L NW CI U NW
#> [1,] 0.2786 0.1199 0.419
#> attr(,"class")
#> [1] "mipool"
```

See for more functions the package website

The main functions of the package are the `df2milist`

, `list2milist`

, `mids2milist`

and the `with.milist`

functions. The `df2milist`

function turns a data frame with multiply imputed datasets into an object of class `milist`

, the `list2milist`

does this for a list with multiply imputed datasets and the `mids2milist`

for objects of class `mids`

. These `milist`

object can than be used with the `with.milist`

function to apply repeated statistical analyses across the multiply imputed datasets. Subsequently, pooling functions are available in the form of separate `pool`

functions.

You can install the development version from GitHub with:

```
# install.packages("devtools")
devtools::install_github("mwheymans/miceafter")
```