| Variable N = 699 |
N (%) Cases With Missing Values | Role |
|---|---|---|
| Age | 0 (0) | Predictor |
| Gender | 0 (0) | Predictor |
| Education | 0 (0) | Predictor |
| Smoking status | 0 (0) | Predictor |
| Alcohol use | 0 (0) | Predictor |
| MMSE score | 0 (0) | Predictor |
| Somatic diseases | 0 (0) | Predictor |
| Weight loss | 5 (0.7) | Predictor and imputed |
| Grip strength | 35 (5.0) | predictor and imputed |
| Walking speed | 48 (6.9) | predictor and imputed |
| Exhaustion | 0 (0) | Predictor |
| Low activity level | 0 (0) | Predictor |
| CES-D follow-up 1 | 53 (7.6) | Predictor |
| CES-D follow-up 2 | 130 (18.6) | Predictor |
| CES-D follow-up 3 | 250 (35.8) | Predictor |
| CES-D baseline | 0 (0) | Predictor |
When patterns of missing data were analyzed, it showed that data was missing by a random pattern and therefore the Fully Conditional Specification Method was used. We used the Fully Conditional Specification Method and created 43 datasets, as 43% of the cases had at least 1 missing value.29 In total, 4.7% of the data were missing. The imputation included the variables that were used in the final model.