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.