Update to improve reproducibility and interpretability: A response to "Machine Learning for Tumor Growth Inhibition"

Reproducibility is an important aspect of pharmacometric research, but complications can occur when complex data management and an understanding of modeling approaches from multiple disciplines are required, such as the case when machine learning (ML), a tumor growth inhibition (TGI) model, and baseline covariates were used to predict overall survival (OS) outcome. In addition, continuous updates of our original work,1 based on more sophisticated ML methodologies, are expected to increase the interpretability of the modeling results.