pool: Pools Estimates by Rubin's Rules In MatchThem: Matching and Weighting Multiply Imputed Datasets

Description

`pool()` pools estimates from the ana;yses done withi neach imputed dataset. The typical sequence of steps to do a matching procedure on the imputed datasets are:

1. 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);

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

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

4. Fit the statistical model of interest on each matched 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 `mipo` class.

Usage

 `1` ```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 of the `mipo` class. Methods for `mipo` objects (e.g., `print()`, `summary`, etc.) are available in mice, which does not need to be attached to use them.

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/

`with()` `mice::pool()`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23``` ```#Loading libraries library(MatchThem) library(survey) #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 = 'ps') #Analyzing the weighted datasets models <- with(weighted.datasets, svyglm(KOA ~ OSP, family = quasibinomial)) #Pooling results obtained from analyzing the datasets results <- pool(models) summary(results) ```