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Evaluating generalizability of practice-changing randomized clinical trials in non-small cell lung cancer using machine learning-based in-silico trials

May 25, 2023


While randomized clinical trials of anticancer drugs have provided valuable insights, they may not reflect the experiences of many real-world patients. However, with recent advances in machine learning (ML) and the growing availability of curated real-world data, it is now possible to simulate trials “in-silico” and assess their generalizability across subpopulations. The objective of this study was to evaluate the generalizability of survival outcomes reported in five phase III trials that led to changes in clinical practice for patients with advanced non-small cell lung cancer in the first-line setting. 

Why this matters

This research addresses a fundamental challenge in cancer research: how to ensure that the results of clinical trials are applicable to the wider patient population. By using innovative approaches like ML to predict overall survival, we can gain a more comprehensive understanding of the benefits and limitations of different treatments for higher risk patients, as well as identify ways to improve patient outcomes and develop more effective and personalized cancer care. 

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