with.mimids: Evaluates an Expression in Matched or Weighted Imputed Datasets

Description

with() function performs a statistical computation on the n imputed datasets of the mimids or wimids objects. The typical sequence of steps to do a matching or weighting procedure on the imputed datasets are:

Impute the missing values by 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 imputed datasets using a matching or weighting model by the matchthem() or weightthem() function, resulting in an object of the mimids or wimids class;
Check the extent of balance of covariates across the datasets;
Fit the statistical model of interest on each dataset by the with() function, resulting in an object of the mira class; and
Pool the estimates from each model into a single set of estimates and standard errors, resulting in an object of the mipo class.

Usage

# S3 method for mimids
with(data, expr, ...)

Arguments

data

This argument specifies an object of the mimids or wimids class, typically produced by a previous call to the matchthem() or weightthem().

expr

This argument specifies an expression of the usual syntax of R formula (it also accepts expressions from survey package, like svyglm(), please note that you shouldn't include the weights = weights argument, see the package vignette for details).

...

Additional arguments to be passed to expr.

Value

This function returns an object of the mira class (multiply imputed repeated analyses).

Details

with() performs a computation on the imputed datasets.

References

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/

Examples

Run this code

# NOT RUN {
#Loading the dataset
data(osteoarthritis)

#Multiply imputing the missing values
imputed.datasets <- mice(osteoarthritis, m = 5, maxit = 10,
                         method = c("", "", "mean", "polyreg",
                                    "logreg", "logreg", "logreg"))

#Matching 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(data = matched.datasets,
               exp = glm(KOA ~ OSP, family = binomial))

#or

#Estimating weights of observations in the multiply imputed datasets
weighted.datasets <- weightthem(OSP ~ AGE + SEX + BMI + RAC + SMK, imputed.datasets,
                                approach = 'within', method = 'ps')

#Analyzing the weighted datasets
models <- with(data = weighted.datasets,
               exp = svyglm(KOA ~ OSP, family = binomial))
# }

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