Zhao Rui, Catalano Paul, DeGruttola Victor G, Michor Franziska
Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts 02115, United States of America.
Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, Massachusetts 02215, United States of America.
PLoS One. 2017 Jul 19;12(7):e0180756. doi: 10.1371/journal.pone.0180756. eCollection 2017.
The dynamics of tumor burden, secreted proteins or other biomarkers over time, is often used to evaluate the effectiveness of therapy and to predict outcomes for patients. Many methods have been proposed to investigate longitudinal trends to better characterize patients and to understand disease progression. However, most approaches assume a homogeneous patient population and a uniform response trajectory over time and across patients. Here, we present a mixture piecewise linear Bayesian hierarchical model, which takes into account both population heterogeneity and nonlinear relationships between biomarkers and time. Simulation results show that our method was able to classify subjects according to their patterns of treatment response with greater than 80% accuracy in the three scenarios tested. We then applied our model to a large randomized controlled phase III clinical trial of multiple myeloma patients. Analysis results suggest that the longitudinal tumor burden trajectories in multiple myeloma patients are heterogeneous and nonlinear, even among patients assigned to the same treatment cohort. In addition, between cohorts, there are distinct differences in terms of the regression parameters and the distributions among categories in the mixture. Those results imply that longitudinal data from clinical trials may harbor unobserved subgroups and nonlinear relationships; accounting for both may be important for analyzing longitudinal data.
肿瘤负荷、分泌蛋白或其他生物标志物随时间的动态变化，常被用于评估治疗效果并预测患者的预后。人们已经提出了许多方法来研究纵向趋势，以便更好地描述患者特征并了解疾病进展。然而，大多数方法都假定患者群体是同质的，并且随着时间推移和患者个体差异，反应轨迹是一致的。在此，我们提出了一种混合分段线性贝叶斯分层模型，该模型同时考虑了群体异质性以及生物标志物与时间之间的非线性关系。模拟结果表明，在三种测试场景中，我们的方法能够根据治疗反应模式对受试者进行分类，准确率超过80%。然后，我们将模型应用于一项针对多发性骨髓瘤患者的大型随机对照III期临床试验。分析结果表明，即使在分配到相同治疗组的患者中，多发性骨髓瘤患者的纵向肿瘤负荷轨迹也是异质性和非线性的。此外，在不同治疗组之间，回归参数以及混合类别中的分布存在明显差异。这些结果表明，临床试验的纵向数据可能包含未观察到的亚组和非线性关系；同时考虑这两者对于分析纵向数据可能很重要。
