Statistical models predict the probability of mortality at 6 and 12 months after second-line treatment. Calibration curves for mortality at (A) 6 and (B) 12 months after starting second-line treatment with (C) an example for a hypothetical individual patient. The solid blue line shows the fit between the observed probabilities of mortality (y axis) with the estimated probabilities of mortality as predicted by the model (x axis) (see Methods for details). The fit is obtained by using locally estimated scatterplot smoothing. This approach is like standard linear least-squares regression, but this simpler model is fit to localized subsets of the data, leading to a more flexible representation of the fit between the predicted and observed outcomes than could be achieved assuming a linear association across the entire span of the data. The dashed red line shows results that would be expected for a perfect correlation. The shaded area shows the pointwise 95% CIs of the observed probabilities of mortality across the range of predicted probabilities. In the example, values for each risk factor are multiplied by the respective coefficients. The sum of the products plus the intercept is used to predict the probability of mortality from the formula in the bottom row. In this example, the predicted probability of mortality is 0.39 at 6 months and 0.58 at 12 months.