An effective macro to analyze non-normal data with missing values

Oregon Convention Center 

Multiple imputation (MI) is a popular experimental procedure for handling missing data, while robust regression implements novel empirical evidence for the decision regarding non-normal or normal variables. By down-weighting the influence of outliers, Robust regression minimize residual impact on the coefficient estimates. A macro developed by the Fortrea Company, combines two methods together to accurately capture this relationship in continuous efficacy laboratory data and protect against potential non‐normality/outliers in the original or imputed dataset. This paper provides an example programming procedure and suggests possible improvements in the macro based on the author's experience.

Multiple imputation

missing data

non-normal

Robust regression 

Section for Statistical Programmers and Analysts