A Machine Learning Approach to Real‐World Time to Treatment Discontinuation Prediction

Abstract

Real‐world time to treatment discontinuation (rwTTD) is an important endpoint measurement of drug efficacy evaluated using real‐world observational data. rwTTD, represented as a set of metrics calculated from a population‐wise curve, cannot be predicted by existing machine learning approaches. Herein, a methodology that enables predicting rwTTD is developed. First, the robust performance of the model in predicting rwTTD across populations of similar or distinct properties with simulated data using a variety of commonly used base learners in machine learning is demonstrated. Then, the robust performance of the approach both within‐cohort and cross‐disease using real‐world observational data of pembrolizumab for advanced lung cancer and head neck cancer is demonstrated. This study establishes a generic pipeline for real‐world time on treatment prediction, which can be extended to any base machine learners and drugs. Currently, there is no existing machine learning approach established for predicting population‐wise rwTTD, despite that it is an essential metric to report real‐world drug efficacy. Therefore, we believe our study opens a new investigation area of rwTTD prediction, and provides an innovative approach to probe this problem and other problems involving population‐wise predictions. An interactive preprint version of the article can be found at: https://doi.org/10.22541/au.166065465.59798123/v1.