![]() ![]() Correlations between covariates and the number of false covariates does not affect the performance of any method, but effect size has an impact. ML achieved similar or better F1 scores than stepwise covariate modeling (SCM) and conditional sampling for stepwise approach based on correlation tests (COSSAC). Methods are applied to different scenarios of covariate influence based on simulated pharmacokinetics data. The F1 score, which measures test accuracy, is used to compare ML and PMX approaches. The performance of the ML models is assessed using receiver operating characteristic (ROC) curves. We compare various classical methods with three machine learning (ML) methods applied to NONMEM empirical Bayes estimates: random forest, neural networks (NNs), and support vector regression (SVR). Here, we demonstrate how this complex selection task could benefit from supervised learning algorithms using importance scores. One of the objectives of Pharmacometry (PMX) population modeling is the identification of significant and clinically relevant relationships between parameters and covariates.
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