Description Usage Arguments Details Value Author(s) References See Also Examples

`with()`

runs a model on the `n`

imputed datasets of the supplied `mimids`

or `wimids`

object. The typical sequence of steps to do a matching procedure on the imputed datasets are:

Impute the missing values using the

`mice()`

function (from the mice package) or the`amelia()`

function (from the Amelia package), resulting in a multiple imputed dataset (an object of the`mids`

or`amelia`

class);Match or weight each imputed dataset using

`matchthem()`

or`weightthem()`

, resulting in an object of the`mimids`

or`wimids`

class;Check the extent of balance of covariates across the matched datasets (using functions in cobalt);

Fit the statistical model of interest on each matched dataset by the

`with()`

function, resulting in an object of the`mimira`

class; andPool the estimates from each model into a single set of estimates and standard errors, resulting in an object of the

`mipo`

class.

1 2 3 4 5 |

`data` |
An |

`expr` |
An expression (usually a call to a modeling function like |

`cluster` |
When a function from survey (e.g., |

`...` |
Additional arguments to be passed to |

`with()`

applies the supplied model in `expr`

to the matched or weighrd imputed datasets, automatically incorporating the (matching) weights when possible. The argument to `expr`

should be of the form `glm(y ~ z, family = quasibinomial)`

, for example, excluding the data or weights argument, which are automatically supplied.

Functions from the survey package, such as `svyglm()`

, are treated a bit differently. No `svydesign`

objcect needs to be supplied because `with()`

automatically constructs and supplies it with the imputed dataset and estimated weights. When `cluster = TRUE`

(or `with()`

detects that pairs should be clustered; see Arguments above), pair membership is supplied to the `ids`

argument of `svydesign()`

.

For generalized linear models, it is always recommended to use `svyglm()`

rather than `glm()`

in order to correctly compute standard errors. For Cox models, `coxph()`

will produce correct standard errors when used with weighting but `svycoxph()`

will produce more accurate standard errors when matching is used.

An object of the `mimira`

class containing the output of the analyses.

Farhad Pishgar and Noah Greifer

Stef van Buuren and Karin Groothuis-Oudshoorn (2011). `mice`

: Multivariate Imputation by Chained Equations in `R`

. *Journal of Statistical Software*, 45(3): 1-67. https://www.jstatsoft.org/v45/i03/

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | ```
#Loading libraries
library(MatchThem)
library(survey)
#Loading the dataset
data(osteoarthritis)
#Multiply imputing the missing values
imputed.datasets <- mice::mice(osteoarthritis, m = 5)
#Matching in the multiply imputed datasets
matched.datasets <- matchthem(OSP ~ AGE + SEX + BMI + RAC + SMK,
imputed.datasets,
approach = 'within',
method = 'nearest')
#Analyzing the matched datasets
models <- with(matched.datasets,
svyglm(KOA ~ OSP, family = binomial),
cluster = TRUE)
``` |

```
Loading required package: MatchIt
Loading required package: WeightIt
Loading required package: grid
Loading required package: Matrix
Loading required package: survival
Attaching package: ‘survey’
The following object is masked from ‘package:graphics’:
dotchart
iter imp variable
1 1 BMI RAC SMK OSP KOA
1 2 BMI RAC SMK OSP KOA
1 3 BMI RAC SMK OSP KOA
1 4 BMI RAC SMK OSP KOA
1 5 BMI RAC SMK OSP KOA
2 1 BMI RAC SMK OSP KOA
2 2 BMI RAC SMK OSP KOA
2 3 BMI RAC SMK OSP KOA
2 4 BMI RAC SMK OSP KOA
2 5 BMI RAC SMK OSP KOA
3 1 BMI RAC SMK OSP KOA
3 2 BMI RAC SMK OSP KOA
3 3 BMI RAC SMK OSP KOA
3 4 BMI RAC SMK OSP KOA
3 5 BMI RAC SMK OSP KOA
4 1 BMI RAC SMK OSP KOA
4 2 BMI RAC SMK OSP KOA
4 3 BMI RAC SMK OSP KOA
4 4 BMI RAC SMK OSP KOA
4 5 BMI RAC SMK OSP KOA
5 1 BMI RAC SMK OSP KOA
5 2 BMI RAC SMK OSP KOA
5 3 BMI RAC SMK OSP KOA
5 4 BMI RAC SMK OSP KOA
5 5 BMI RAC SMK OSP KOA
Matching Observations | dataset: #1 #2 #3 #4 #5
```

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