Application of machine learning methods in clinical trials for precision medicine. Issue 1 (8th February 2022)

• Record Type: Journal Article
• Authors: Wang, Yizhuo; Carter, Bing Z; Li, Ziyi; Huang, Xuelin
• Is Part Of: JAMIA open. Volume 5:Issue 1(2022)
• Publication Date: 2022-02-08
• Subjects: clinical trial -- adaptive design -- machine learning -- precision medicine; Medical informatics -- Periodicals; 610.285
• DOI: 10.1093/jamiaopen/ooab107
• Languages: English
• ISSNs: 2574-2531

Abstract

Abstract: Objective: A key component for precision medicine is a good prediction algorithm for patients' response to treatments. We aim to implement machine learning (ML) algorithms into the response-adaptive randomization (RAR) design and improve the treatment outcomes. Materials and Methods: We incorporated 9 ML algorithms to model the relationship of patient responses and biomarkers in clinical trial design. Such a model predicted the response rate of each treatment for each new patient and provide guidance for treatment assignment. Realizing that no single method may fit all trials well, we also built an ensemble of these 9 methods. We evaluated their performance through quantifying the benefits for trial participants, such as the overall response rate and the percentage of patients who receive their optimal treatments. Results: Simulation studies showed that the adoption of ML methods resulted in more personalized optimal treatment assignments and higher overall response rates among trial participants. Compared with each individual ML method, the ensemble approach achieved the highest response rate and assigned the largest percentage of patients to their optimal treatments. For the real-world study, we successfully showed the potential improvements if the proposed design had been implemented in the study. Conclusion: In summary, the ML-based RAR design is a promising approach for assigning more patients to their personalized effective treatments, which makes the clinical trial more ethical and appealing. These features are especially desirable for late-stage cancer patients who have failed all the Food and Drug Administration (FDA)-approved treatment options and only can get new treatments through clinical trials. Lay Summary: In a typical controlled clinical trial, patients are equally randomized to receive different treatments. However, it is possible that one treatment demonstrates advantages over others during the trial. Utilizing that information can benefit subsequent patients. This is why response-adaptive randomization (RAR), which allows uneven treatment assignment probabilities based on existing knowledge, has become popular recently. A key component of RAR is a good prediction algorithm for patients' response to treatments. Previous works have explored using machine learning (ML) to predict treatment response, but few incorporated ML methods into RAR. This study implements 9 commonly used ML methods into RAR trial designs. We further present an ML-ensemble RAR design that builds upon the majority consensus of the 9 ML methods' predictions. Extensive simulation studies and real-world applications show that using ML methods in RAR leads to the assignment of more patients to their optimal treatments, increasing the overall response rate. The proposed method will become a useful tool for future clinical trial design in the era of precision medicine.