Margins after mice?

Question

I would like to apply the margins function to imputed data (I used mice), but it seems not possible. Do you know if a function exists that calculates marginal effects with imputed data?

Thank you!

Edit: I have modified my code as follows: is it correct? Thank you!

ddply(data_pooled, "imp", function(x){ 
  logregress <-  glm(y ~ var1 + var2 + var3, family = binomial(link="logit"), data=data_pooled)
  
  margin_mod <- margins(logregress, type="response")
  })

margin_summary <- summary(MIcombine(margin_mod))

Answer 1

The general approach to analysis of missing data using multiple imputation is

• create several complete datasets, let's say $m$, using whatever multiple imputation alogorithm you choose

• perform the final analysis model (eg a regression model) on each complete dataset. That is, you would run $m$ models.

• pool the results of the analyses. With a linear regression model you would simply average all the estimates of interest to find the pooled estimate. For standard errors, the typical approach is to use Rubin's rules, which incorporate uncertainty both within, and between, imputations. If the sampling distribution of the estimate of interest does not follow a normal distribution, then other methods can be employed. See this question and answer for further details: Applying Rubin's rule for combining multiply imputed datasets