## 7 Different Approaches to Dealing with Missing Data in Cohorts  
Different types of missing data in phenotypic and genotypic databases can appear in cohorts: these include, but are not limited to, the following: irrelevant non-response, responses of participants that were excluded from the study in the follow up, irrelevant missing structural data (when data are irrelevant in the context), responses of participants such as \"do not know\" responses, no answer to a specific item, and missing data due to error codes.  
There are several approaches for dealing with missing data: complete-case analyses, last observational carried forward (LOCF) method, mean value substitution method, missing indicator method, and multiple imputations (MI).  
The complete case-analysis only is comprised of applicants with full data in all waves of data collection, thus possibly decreasing the accuracy of the estimates of the exposure-outcome relations [13]. To be effective, complete case analyses should assume that applicants with missing data can be considered to be a random sample of those that were intended to be observed (generally referred as missing completely at random (MCAR)) [13], or at least that the probability of data being missing does not dependent on the observed value [39]. Further, LOCF is a method of imputing missing data in longitudinal studies with the non-missing value from the previously completed time-point for the same individual, since the imputed values are unrelated to a subject's other measurements. The mean value substitution replaces the missing value with the average value available from the other individual time-points of a longitudinal study [40]. The missing indicator method comprises an additional category for the analysis created for applicants with missing data [41].  
Missing data can be handled by means of multiple imputation [42; 43; 44; 45]. MI methods are used to address missing data and its assumptions are more flexible than those of complete case analysis [46]. The principle of MI is to substitute missing observations with plausible values multiple times and generate complete data sets [47]. Every single complete data set is individually analyzed and the effects of the analyses are then assembled. MI results are consistent when the missing mechanism satisfies the MCAR and the missing at random (MAR) assumptions [48; 49]. Multiple imputation consists of three steps. The first step is (1) to determine which variables are used for imputation. The variables used for imputation should be selected on the basis of the presentation of their missing information as MAR [43], that is, whether or not a score that is missing depend on the missing value [42]. The variables that cause the missingness are unknown to the researcher unless missingness is, to some extent, expected. Practically, variables are selected in a way that expected to be good predictors containing missing values. One can choose the number of variables and which variables to use, but there is no alternate way to assess whether MAR is achieved, and MAR is an assumption. Binary or ordinal variables may be imputed under a normality assumption and then rounded off to discrete values. If a variable is right skewed, it might be modeled on a logarithmic scale and then transformed back to the original scale after imputation [42]. We should impute variables that are functions of other (incomplete) variables. Several data sets consist of transformed variables, sum scores, interaction variables, ratio's, and so on. It can be useful to integrate the transformed variables into the multiple imputation algorithms [50]. The second step is (2) to generate imputed data matrices. One of the tools that can be used is the R package Multiple Imputation by Chained Equations (MICE) [50], which uses an iterative procedure in which each variable is sequentially imputed and restricted on the real and imputed values of the other variables. The third step of the multiple imputation procedure is (3) to analyze each imputed data set as desired and pool the results [51].