Liu Molei, Sun Jiehuan, Herazo-Maya Jose D, Kaminski Naftali, Zhao Hongyu
Department of Biostatistics, Harvard School of Public Health, Harvard University, Boston, MA 02115, U.S.A.
Department of Biostatistics, Yale University, New Haven, CT 06510, U.S.A.
Stat Biosci. 2019 Dec;11(3):614-629. doi: 10.1007/s12561-019-09256-0. Epub 2019 Sep 23.
Joint models for longitudinal biomarkers and time-to-event data are widely used in longitudinal studies. Many joint modeling approaches have been proposed to handle different types of longitudinal biomarkers and survival outcomes. However, most existing joint modeling methods cannot deal with a large number of longitudinal biomarkers simultaneously, such as the longitudinally collected gene expression profiles. In this article, we propose a new joint modeling method under the Bayesian framework, which is able to analyze longitudinal biomarkers of high dimension. Specifically, we assume that only a few unobserved latent variables are related to the survival outcome and the latent variables are inferred using a factor analysis model, which greatly reduces the dimensionality of the biomarkers and also accounts for the high correlations among the biomarkers. Through extensive simulation studies, we show that our proposed method has improved prediction accuracy over other joint modeling methods. We illustrate the usefulness of our method on a dataset of idiopathic pulmonary fibrosis patients in which we are interested in predicting the patients' time-to-death using their gene expression profiles.
用于纵向生物标志物和事件发生时间数据的联合模型在纵向研究中被广泛使用。已经提出了许多联合建模方法来处理不同类型的纵向生物标志物和生存结局。然而，大多数现有的联合建模方法不能同时处理大量的纵向生物标志物，例如纵向收集的基因表达谱。在本文中，我们提出了一种在贝叶斯框架下的新联合建模方法，该方法能够分析高维纵向生物标志物。具体而言，我们假设只有少数未观察到的潜在变量与生存结局相关，并且使用因子分析模型推断潜在变量，这大大降低了生物标志物的维度，同时也考虑了生物标志物之间的高度相关性。通过广泛的模拟研究，我们表明我们提出的方法比其他联合建模方法具有更高的预测准确性。我们在特发性肺纤维化患者的数据集中说明了我们方法的实用性，在该数据集中我们有兴趣使用患者的基因表达谱预测患者的死亡时间。
