matchthem() performs matching in the supplied imputed datasets, given as
amelia objects, by running
MatchIt::matchit() on each of the imputed datasets with the supplied arguments.
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This argument specifies the datasets containing the exposure indicator and the potential confounders called in the
The approach used to combine information across imputed datasets. Currently,
This argument specifies a matching method. Currently,
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
Arguments passed to
Additional arguments passed to
amelia object is supplied to
datasets, it will first 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
approach are passed directly to
matchit(). With the across method, the estimated propensity scores are averaged across imputations and re-supplied to another set of calls to
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 imputed dataset.
Farhad Pishgar and Noah Greifer
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. https://www.jstatsoft.org/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
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#1 #Loading libraries library(MatchThem) #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 libraries library(Amelia) library(MatchThem) #Loading the dataset data(osteoarthritis) #Multiply imputing the missing values imputed.datasets <- 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')
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