Statsols Blog

An Overview of Multiple Imputation in SOLAS for Missing Data 5.0

In our previous post we discussed the pervasive problem of missing data in data analysis. To recap quickly, in a data set with 5 variables measured at the start of a study and monthly for six months, if each variable is 95% complete with a random 5% of the values missing, then the proportion of cases that are expected to be incomplete are 1-(.95)^35= 0.834. That is, only 17% of the cases would be complete and with traditional complete case analysis, you would then lose 83% of your data.

How It Works?

With Solas 5.0TM, missing values in a data set are filled-in with plausible estimates to produce a complete data set that can be analyzed using complete-data inferential methods and designed to accommodate a range of missing data scenarios in both longitudinal and single-observation study designs.

Topics: Missing Data Multiple Imputation Hot Deck Imputation