Lin Xiao, Lu Tao, Yan Fangrong, Li Ruosha, Huang Xuelin
Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing 210009, P.R. China.
Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, U.S.A.
Biometrics. 2018 Dec;74(4):1482-1491. doi: 10.1111/biom.12876. Epub 2018 Mar 30.
Predicting patient life expectancy is of great importance for clinicians in making treatment decisions. This prediction needs to be conducted in a dynamic manner, based on longitudinal biomarkers repeatedly measured during the patient's post-treatment follow-up period. The prediction is updated any time a new biomarker measurement is obtained. The heterogeneity across patients of biomarker trajectories over time requires flexible and powerful approaches to model noisy and irregularly measured longitudinal data. In this article, we use functional principal component analysis (FPCA) to extract the dominant features of the biomarker trajectory of each individual, and use these features as time-dependent predictors (covariates) in a transformed mean residual life (MRL) regression model to conduct dynamic prediction. Simulation studies demonstrate the improved performance of the transformed MRL model that includes longitudinal biomarker information in the prediction. We apply the proposed method to predict the remaining time expectancy until disease progression for patients with chronic myeloid leukemia, using the transcript levels of an oncogene, BCR-ABL.
预测患者的预期寿命对于临床医生做出治疗决策至关重要。这种预测需要以动态方式进行，基于在患者治疗后随访期间反复测量的纵向生物标志物。每当获得新的生物标志物测量值时，预测就会更新。随着时间的推移，生物标志物轨迹在患者之间的异质性需要灵活且强大的方法来对噪声大且测量不规则的纵向数据进行建模。在本文中，我们使用功能主成分分析（FPCA）来提取每个个体生物标志物轨迹的主要特征，并将这些特征用作变换后的平均剩余寿命（MRL）回归模型中的时间依赖性预测因子（协变量）以进行动态预测。模拟研究表明，在预测中包含纵向生物标志物信息的变换后的MRL模型具有更好的性能。我们应用所提出的方法，使用一种致癌基因BCR-ABL的转录水平来预测慢性髓性白血病患者直到疾病进展的剩余预期时间。
