Chapter 9 Multiple Imputation of Missing Data

In this chapter, you will learn:

  • Missing data concepts and terminology;
  • How to create multiply imputed datasets; and
  • How to carry out the following analyses accounting for missing data via multiple imputation:
    • Descriptive statistics;
    • Linear regression;
    • Binary logistic regression; and
    • Cox proportional hazards regression.

This chapter assumes that you have read the chapters on these statistical analysis methods.

To use the code in this chapter, first load the tidyverse, mice (van Buuren and Groothuis-Oudshoorn 2011, 2023), and miceadds (Robitzsch, Grund, and Henke 2023) libraries along with the file Functions_rmph.R (downloadable from RMPH Resources).

library(tidyverse)
library(mice)
library(miceadds)
source("Functions_rmph.R")

References

Robitzsch, Alexander, Simon Grund, and Thorsten Henke. 2023. Miceadds: Some Additional Multiple Imputation Functions, Especially for Mice. https://github.com/alexanderrobitzsch/miceadds.
van Buuren, Stef, and Karin Groothuis-Oudshoorn. 2011. mice: Multivariate Imputation by Chained Equations in r.” Journal of Statistical Software 45 (3): 1–67. https://doi.org/10.18637/jss.v045.i03.
———. 2023. Mice: Multivariate Imputation by Chained Equations. https://github.com/amices/mice.