1 Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing, China.
2 Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Stat Methods Med Res. 2019 Apr;28(4):1216-1229. doi: 10.1177/0962280217753466. Epub 2018 Feb 5.
Optimal therapeutic decisions can be made according to disease prognosis, where the residual lifetime is extensively used because of its straightforward interpretation and formula. To predict the residual lifetime in a dynamic manner, a longitudinal biomarker that is repeatedly measured during the post-baseline follow-up period should be included. In this article, we use functional principal component analysis, a powerful and flexible tool, to handle irregularly measured longitudinal data and extract the dominant features over a specific time interval. To capture the time-dependent trajectory pattern, a series of moving time windows are used to estimate window-specific functional principal component analysis scores, which are then combined with a quantile residual lifetime regression model to facilitate dynamic prediction. Estimation of this regression model can be achieved by solving estimating equations with the help of locating the minimizer of the L-type function. Simulation studies demonstrate the advantages of our proposed method in both calibration and discrimination under various scenarios. The proposed method is applied to data from patients with chronic myeloid leukemia to illustrate its practicality, where we dynamically predict quantile residual lifetimes with longitudinal expression levels of an oncogene, BCR-ABL.
可以根据疾病预后做出最佳治疗决策，由于其解释简单、公式明了，因此广泛使用剩余寿命。为了动态预测剩余寿命，应该包含在基线后随访期间反复测量的纵向生物标志物。在本文中，我们使用功能主成分分析，这一强大而灵活的工具来处理不规则测量的纵向数据，并在特定时间段内提取主要特征。为了捕获时间相关的轨迹模式，使用一系列移动时间窗口来估计窗口特定的功能主成分分析得分，然后将其与分位数剩余寿命回归模型相结合，以促进动态预测。通过求解 L 型函数的最小值来帮助定位，从而可以利用估计方程来估计这个回归模型。模拟研究表明，在各种情况下，我们提出的方法在校准和判别方面都具有优势。该方法应用于慢性髓性白血病患者的数据，以说明其实际应用，我们使用癌基因 BCR-ABL 的纵向表达水平来动态预测分位数剩余寿命。
