However, this finding is not robust at all: in specifications 4 and 5, both the statistical significance and the magnitude of the wage ratio variable are quite low at all values of age at arrival.
Multivariable analysis showed that the best-fitting model included 3 factors independently correlated to ARC: trauma patients, cut-off values of age ≤58 years, and CKD-EPI more than 108 ml/min/1083 m2.
The effect of wage ratio on migration duration decision is displayed in panel (b) of Table 3, also at the time of arrival and at 20 years of residence for various values of age.
However, multivariable analysis showed that 3 factors were independently correlated to ARC and useful to screen ARC: trauma patients, cut-off values of age ≤58 years, and CKD-EPI more than 108 m²/m²n/1.73 m².
The analysis of accuracy of age classification employed the most commonly used Mean Absolute Error (MAE) metric, which calculates the average absolute deviation of the age values predicted by the decision function for the objects included in the test set from the true values of age [13, 24, 26].
The values of age and size were continuous.
A quartile division of linear and non linear scores was performed in order to identify 4 classes of predicted EWL for both models, on the basis of the values of age and selected psychological variables (Fig. 4 5).
They were identified by using the residuals from a regression model of annual weight change on baseline values of age, weight and height, smoking status (current/former/never smokers), and follow-up time.
In table 3 mean values of age, and metabolic traits are given for the pooled group of participants, and also separately for stressed and non-stressed participants for each survey.
Values of age are given mean ± s.d.
Values of age are mean ± standard deviation.
