The output of the misstable nested presented immediately below clearly shows that the missing values of the different variables were not nested because 10 statements describe the missing value pattern, thereby confirming the arbitrary nature of the missing data pattern (see Misstable nested).
Table 2 shows the results of the missing value analysis for study questions 1 and 2. For students in both components of the course, there was no significant relationship between HADS-D on entry and subsequent study participation.
This analysis showed that the missing values in our data occurred primarily and progressively in variables that were measured longitudinally that is, response and potential predictors variables.
The missing values for the outcome variables are around 23%% and non-response analysis showed that missing values were more likely to be male, older, have a lower educational level and live in Wallonia or Brussels.
The missing values amounted to 3.3%%.
Figure 3 shows the proportions of missing values for the variables under consideration in our analysis of the RAD data.
Table 2 shows the number of missing values in the FACT-G, FLIC and EORTC QLQ-C30 by presentation order.
The PedsQL 4.0 Generic Core Scales and the PedsQL 3.0 End-Stage Renal Disease minimalshowed missing missing values in the current study, which supported feasibility.
In addition, the PedsQL 4.0 Generic Core Scales and the PedsQL 3.0 End Stage Renal Disease Module showed minimal missing values in the current study, supporting feasibility [ 3].
Table C in appendix 1 shows the number of missing values for all variables incorporated in the original model.
