R, mitools::MIcombine, what is the reason for no p-values?

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I am currently running a simple linear regression model with 5 multiply imputed datasets in R.

E.g. model <- with(imp, lm(outcome ~ exposure))

To pool the summary estimates I could use the command summary(mitools::MIcombine(model)) from the mitools package. However, this does not give results for p-values. I could also use the command summary(pool(model)) from the mice package and this does give results for p-values.

Because of this, I am wondering if there is a specific reason why MIcombine does not produce p-values?

r-mice imputation summary linear-regression r
1 Answers

After looking through the documentation, it doesn't seem like there is a particular reason that the mitools library doesn't provide p-values. Although, the package's focus is on imputation, not model results.

However, you don't need either of these packages to see your results–along with the per model p-values. I started writing this as a comment but decided to include the code. If you weren't aware...you can use base R's summary. I realize that the output of mice is comparative, as is mitools. I thought it was important enough to mention this, as well.

If the output of your call is model, then this will work.

library(tidyverse)
map(1:length(model), ~summary(model[.x]))
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