Table 1.
Number of variables organized by category before and after the procedure for variable selection.
Fig 1.
10-fold cross validation scheme.
Illustrative scheme of the 10-fold cross validation procedure. During the 10 iterations each sample receives a probabilistic prediction. All these predictions are then used to assess the Lasso model.
Fig 2.
ROC curves of the Lasso model and the other risk indicators (number of previous falls, gait speed, SPPB, and FRAT-up) when predicting for single (left panel) and multiple (right panel) falls.
Table 2.
Discriminative ability of five fall risk indicators.
Comparisons with FRAT-up and Lasso model were made with DeLong tests for paired AUCs.
Fig 3.
Representative distributions of the Lasso model.
Histogram showing the predictive distributions (probability mass functions) on the number of falls for four samples at the 2.5th, 10th, 90th, and 97.5th percentiles of the Lasso risk score. These are compared with the distribution of the number of falls in the InCHIANTI population (in gray).
Fig 4.
Assessment of Lasso model calibration.
(A) Reliability diagram: observed vs predicted fall rate, obtained from grouping samples according to deciles on the risk score; error bars indicate 95% confidence intervals. (B) Marginal calibration plot: observed and predicted number of samples vs number of falls. (C) Histogram of the probability integral transform.
Fig 5.
Performance of the model when constraining the maximum number of variables to be included in the model. Left panel, left axis: AUC for single falls (black empty circles), AUC for multiple falls (black filled circles). Right panel, left axis: MSE (black filled circles). Both panels, right axes: mean number of variables that were actually included in the models (blue circles).