Addressing estimands in clinical studies involves handling of intercurrent events and often multiple imputation methods are applied to handle missing data. In Novo Nordisk more and more programming tasks are done in R, but still multiple imputation methods are conducted using SAS. We look into the possibilities and obstacles of conducting multiple imputation using R and SAS interchangeably. The aim is to make it possible for the statisticians to choose between SAS and R based on either personal preferences or on which tool is best suited for the specific task. More specifically, the approach has been to simulate data, and to compare multiple imputation results from SAS (PROC MI and PROC MIANALYZE) to results from R (MICE package).
Claus Dethlefsen
Novo Nordisk
Co-existence of R and SAS for multiple imputation in Novo Nordisk
Addressing estimands in clinical studies involves handling of intercurrent events and often multiple imputation methods are applied to handle missing data. In Novo Nordisk more and more programming tasks are done in R, but still multiple imputation methods are conducted using SAS. We look into the possibilities and obstacles of conducting multiple imputation using R and SAS interchangeably. The aim is to make it possible for the statisticians to choose between SAS and R based on either personal preferences or on which tool is best suited for the specific task. More specifically, the approach has been to simulate data, and to compare multiple imputation results from SAS (PROC MI and PROC MIANALYZE) to results from R (MICE package).