matchthem {MatchThem} | R Documentation |
Matches Multiply Imputed Datasets
Description
matchthem()
performs matching in the supplied multiply imputed datasets, given as mids
or amelia
objects, by running MatchIt::matchit()
on each of the multiply imputed datasets with the supplied arguments.
Usage
matchthem(
formula,
datasets,
approach = "within",
method = "nearest",
distance = "glm",
link = "logit",
distance.options = list(),
discard = "none",
reestimate = FALSE,
...
)
Arguments
formula |
A |
datasets |
This argument specifies the datasets containing the exposure and the potential confounders called in the |
approach |
The approach that should be used to combine information in multiply imputed datasets. Currently, |
method |
This argument specifies a matching method. Currently, |
distance |
The method used to estimate the distance measure (e.g., propensity scores) used in matching, if any. Only options that specify a method of estimating propensity scores (i.e., not |
link , distance.options , discard , reestimate |
Arguments passed to |
... |
Additional arguments passed to |
Details
If an amelia
object is supplied to datasets
, it will be transformed into a mids
object for further use. matchthem()
works by calling mice::complete()
on the mids
object to extract a complete dataset, and then calls MatchIt::matchit()
on each one, storing the output of each matchit()
call and the mids
in the output. All arguments supplied to matchthem()
except datasets
and approach
are passed directly to matchit()
. With the "across"
approach, the estimated propensity scores are averaged across multiply imputed datasets and re-supplied to another set of calls to matchit()
.
Value
An object of the mimids()
(matched multiply imputed datasets) class, which includes the supplied mids
object (or an amelia
object transformed into a mids
object if supplied) and the output of the calls to matchit()
on each multiply imputed dataset.
Author(s)
Farhad Pishgar and Noah Greifer
References
Daniel Ho, Kosuke Imai, Gary King, and Elizabeth Stuart (2007). Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference. Political Analysis, 15(3): 199-236. https://gking.harvard.edu/files/abs/matchp-abs.shtml
Stef van Buuren and Karin Groothuis-Oudshoorn (2011). mice
: Multivariate Imputation by Chained Equations in R
. Journal of Statistical Software, 45(3): 1-67. doi:10.18637/jss.v045.i03
Gary King, James Honaker, Anne Joseph, and Kenneth Scheve (2001). Analyzing Incomplete Political Science Data: An Alternative Algorithm for Multiple Imputation. American Political Science Review, 95: 49–69. https://gking.harvard.edu/files/abs/evil-abs.shtml
See Also
Examples
#1
#Loading the dataset
data(osteoarthritis)
#Multiply imputing the missing values
imputed.datasets <- mice::mice(osteoarthritis, m = 5)
#Matching the multiply imputed datasets
matched.datasets <- matchthem(OSP ~ AGE + SEX + BMI + RAC + SMK,
imputed.datasets,
approach = 'within',
method = 'nearest')
#2
#Loading the dataset
data(osteoarthritis)
#Multiply imputing the missing values
imputed.datasets <- Amelia::amelia(osteoarthritis, m = 5,
noms = c("SEX", "RAC", "SMK", "OSP", "KOA"))
#Matching the multiply imputed datasets
matched.datasets <- matchthem(OSP ~ AGE + SEX + BMI + RAC + SMK,
imputed.datasets,
approach = 'across',
method = 'nearest')