MICE vs PPCA: Missing data imputation in healthcare

Retrospective analyses of real-world clinical data face challenges owing to the absence of some data elements. Historically, missing data was addressed by first classifying its presence into one of three categories: missing completely at random (MCAR), missing at random (MAR) and missing not at random (MNAR). Imputation techniques continue to be developed and tested to gauge their capacity to mitigate the negative impact of missing data types on analyses and their results. This study undertook a comparison of two techniques of data imputation: probabilistic principal component analysis (PPCA) and multiple imputation using chained equations (MICE).