The plot of the improvements in ASE after the introduction of the various factors is key as it shows that the model including the line of therapy alone is a substantial improvement compared to the null model (with intercept only) but that adding any of the other factors determines only further small improvements to the predictive performance.
Adjusting ΔRESPPP for various estimates of extramural vascular pressure variations (ΔRESPPP/Pplat, ΔRESPPP/driving pressure, and ΔRESPPP/Vt ratios) did not lead to major improvement in predictive performance.
Diffusion tubes are relatively inexpensive and thus more prevalent than automatic monitors in many urban environments, and the subsequent large increase in the number of spatial locations at which NO2 is measured leads to improvements in predictive performance.
Lastly, we calculated the Integrated Discrimination Improvement (IDI) and the Net Reclassification Improvement NRII) [ 12] to quantify the improvement in predictive performance attained by adding the trend covariates to the existing model.
The two pRC features in the model were statistically highly significant (t-test yielding p-values of 1.81 10−5 and 0.0011 for Monogram and Erlangen predictions, respectively), but the resulting improvement in predictive performance, compared to the model without pRC, was marginal.
Compared to individual models, improvement in predictive performance of approximately 10.5% (R2test of 0.685) was observed.
The principle objective of this paper is to demonstrate the improvement in predictive performance of stock market and verify the works of our proposed model in comparison with other optimized models.
An important finding in this study, as in our previous study, is the marked improvement in predictive performance due to the inclusion of clinical factors such as age and gender.
Restricting the gold standard to the subset of TarBase [23], [24] that excludes the high-throughput assays, we observed that the improvement in predictive performance for ExprTarget relative to the individual methods continues to hold robustly (Figure S2).
The weighted G matrices that yielded the overall best accuracies were those informed with realized marker effects from Ridge Regression and Bayesian LASSO, while discriminating markers based on their P-value led to average or null improvement of predictive performance.
