Guvakova Marina A
Department of Surgery, Division of Endocrine & Oncologic Surgery, Harrison Department of Surgical Research, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA.
Oncoscience. 2019 Dec 23;6(11-12):383-385. doi: 10.18632/oncoscience.494. eCollection 2019 Nov.
In clinical research, determining cutoff values for continuous variables in test results remains challenging, particularly when considering candidate biomarkers or therapeutic targets for disease. Distribution of a continuous variable into two populations is known as dichotomization and has been commonly used in clinical studies. We recently reported a new method for determining multiple cutoffs for continuous variables. The development of this original approach was based on fitting Gaussian Mixture Models (GMM) onto real-world clinical data. We also explored how to leverage Bayesian probability to minimize uncertainty while classifying individual patients into respective subpopulations. In addition, we investigated the performance of the proposed method for the distribution of classical prognostic markers in breast cancer. Finally, we applied the proposed method to analyze a candidate marker and a target for cancer therapy. Here, we present an overview of this method and our prospects for its implementation in biomedical and clinical research.
在临床研究中，确定测试结果中连续变量的临界值仍然具有挑战性，尤其是在考虑疾病的候选生物标志物或治疗靶点时。将连续变量分为两个群体的过程称为二分法，在临床研究中已被广泛使用。我们最近报告了一种确定连续变量多个临界值的新方法。这种原始方法的开发基于将高斯混合模型（GMM）应用于真实世界的临床数据。我们还探索了如何利用贝叶斯概率在将个体患者分类到各自亚群时最小化不确定性。此外，我们研究了该方法在乳腺癌经典预后标志物分布方面的性能。最后，我们应用该方法分析了一种癌症治疗的候选标志物和靶点。在此，我们概述了该方法及其在生物医学和临床研究中的应用前景。
