Solved – How to combine/pool binomial confidence intervals after multiple imputation

Question

After I multiply imputed my dataset m times I wanted to calculate a binomial proportion confidence interval. How I can I combine the various estimates of the confidence intervals while taking Rubins rules into account?

Answer 1

This is indeed an interesting problem. The issue is that the standard errors that are based on the central limit theorem for proportions are often undesirable because proportions are a computed quantity and for that reason exhibit skewed uncertainty over sampling. The Wilson score, such as you mentioned, gets around the skewness by estimating a different quantity than the standard proportion $k/n$. What you need to use Rubin's rules is an estimate of the within-imputation variance of this transformed proportion, which is just the variance/standard error estimated on a single dataset, along with the transformed proportion itself for each dataset.

So for the Wilson score interval, you first need to calculate the transformed estimate $ \hat{p} + \frac{1}{2n}z^2 $ and then separately the variance, which from your formula is $ (z\sqrt{\frac{1}{n} \hat{p}(1-\hat{p}) + \frac{1}{4n^2}z^2})^2 $

That will give you estimates of the transformed parameter and the transformed parameter's variance for each of $m$ datasets.

You can then combine these estimates using some of the available R tools, such as mi.meld from Amelia or mice as you mentioned or the R package mitools. Then once you have the transformed parameters, you can compute the confidence interval based on the newly derived variance/parameter estimate.

This would be easier if these R packages supplied the transformed estimates instead of just the confidence intervals, but you can probably dig them out of the associated R code.