pool {MatchThem}

Pools Estimates by Rubin's Rules

Description

pool() pools estimates from the analyses done within each multiply imputed dataset. The typical sequence of steps to do a matching or weighting procedure on multiply imputed datasets are:

1. Multiply impute the missing values using the mice() function (from the mice package) or the amelia() function (from the Amelia package), resulting in a multiply imputed dataset (an object of the mids or amelia class);

2. Match or weight each multiply imputed dataset using matchthem() or weightthem(), resulting in an object of the mimids or wimids class;

3. Check the extent of balance of covariates in the datasets (using functions from the cobalt package);

4. Fit the statistical model of interest on each dataset by the with() function, resulting in an object of the mimira class; and

5. Pool the estimates from each model into a single set of estimates and standard errors, resulting in an object of the mimipo class.

Usage

pool(object, dfcom = NULL)

Arguments

object

An object of the mimira class (produced by a previous call to with()).

dfcom

A positive number representing the degrees of freedom in the data analysis. The default is NULL, which means to extract this information from the fitted model with the lowest number of observations or the first fitted model (when that fails the parameter is set to 999999).

Details

pool() function averages the estimates of the model and computes the total variance over the repeated analyses by Rubin’s rules. It calls mice::pool() after computing the model degrees of freedom.

Value

This function returns an object from the mimipo class. Methods for mimipo objects (e.g., print(), summary(), etc.) are imported from the mice package.

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. doi:10.18637/jss.v045.i03

See Also

with()

mice::pool()

Examples

#Loading libraries
#Loading the dataset
data(osteoarthritis)

#Multiply imputing the missing values
imputed.datasets <- mice::mice(osteoarthritis, m = 5)

#Weighting the multiply imputed datasets
weighted.datasets <- weightthem(OSP ~ AGE + SEX + BMI + RAC + SMK,
                                imputed.datasets,
                                approach = 'within',
                                method = 'glm')

#Analyzing the weighted datasets
models <- with(weighted.datasets,
               WeightIt::glm_weightit(KOA ~ OSP,
                                      family = binomial))

#Pooling results obtained from analyzing the datasets
results <- pool(models)
summary(results)