Model development
A prediction model was developed using multivariable binary logistic regression with backward stepwise selection, excluding variables with p-values > 0.10. We excluded predictors with less than 0.010 contribution to total model performance, expressed in area under the ROC curve (AUC). Categories were only replaced by calcium richer subcategories when substitution led to an increase in AUC. We used the final regression output to calculate individual probabilities of having an adequate calcium intake.
Model discrimination was assessed using the AUC. We used the Hosmer and Lemeshow test and Brier score as measures for goodness of fit. Internal validity was assessed through a bootstrapping procedure using 5 bootstrap samples, resulting in optimism corrected estimates of model performance, which are presented in the results section. Model calibration was assessed by a calibration plot, comparing the predicted probabilities of having an adequate calcium intake with the actual proportion of participants with an adequate intake.