Integrating real‐world data and machine learning: A framework to assess covariate importance in real‐world use of alternative intravenous dosing regimens for atezolizumab

Abstract

Abstract The increase in the availability of real‐world data (RWD), in combination with advances in machine learning (ML) methods, provides a unique opportunity for the integration of the two to explore complex clinical pharmacology questions. Here we present a recently developed RWD/ML framework that utilizes ML algorithms to understand the influence and importance of various covariates on the use of a given dose and schedule for drugs that have multiple approved dosing regimens. To demonstrate the application of this framework, we present atezolizumab as a use case on account of its three approved alternative intravenous (IV) dosing regimens. As expected, the real‐world use of atezolizumab has generally been increasing since 2016 for the 1200 mg every 3 weeks regimen and since 2019 for the 1680 mg every 4 weeks regimen. Out of the ML algorithms evaluated, XGBoost performed the best, as measured by the area under the precision–recall curve, with an emphasis on the under‐sampled class given the imbalance in the data. The importance of features was measured by Shapley Additive exPlanations (SHAP) values and showed metastatic breast cancer and use of protein‐bound paclitaxel as the most correlated with the use of 840 mg every 2 weeks. Although patient usage data for alternative IV dosing regimens are still maturing, these analyses provide initial insights on the use of atezolizumab and set up a framework for the re‐analysis of atezolizumab (at a future data cut) as well as application to other molecules with approved alternative dosing regimens.