Chapter 11 - Missing Data in Longitudinal Studies

Summary

In Chapter 11 the problem of missing data is discussed. Missing data always occurs in longitudinal studies and can be divided based on the missing data mechanism: missing completely at random (MCAR), missing at random (MAR) and missing not at random (MNAR). The problem of the distinction in missing data mechanisms is that it is highly theoretical. More important is the distinction between informative and non-informative missing data. An important part of this chapter deals with imputation methods, such as last value carries forward and multiple imputation. An important conclusion of example studies shown in this chapter is that multiple imputation is, in general, not necessary for missing data in longitudinal studies. It is even better not to impute the missing data and us mixed model analysis for the longitudinal data analysis. In this chapter it is also shown that mixed model analysis deals slightly better with missing data than GEE analysis, although the differences between the two methods are not as great as often suggested.