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

```
library(miceafter)
library(magrittr)
%>%
lbpmilr df2milist(impvar="Impnr") %>%
with(expr=levene_test(Pain ~ factor(Carrying))) %>%
pool_levenetest(method="D1")
#> 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'
<- df2milist(lbpmilr, impvar="Impnr")
imp_dat
# Step 2: Do repeated analyses across multiply imputed datasets
<- with(imp_dat,
ra expr=propdiff_wald(Chronic ~ Radiation, strata = TRUE))
# Step 3: Pool repeated test results
<- pool_propdiff_nw(ra)
res
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")
::install_github("mwheymans/miceafter") devtools
```

Cite the package as:

```
Heymans (2021). miceafter: Data Analysis and Pooling after Multiple Imputation.
Martijn W 0.1.0. https://mwheymans.github.io/miceafter/ R package version
```