Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing, China.
Stat Med. 2021 Apr 15;40(8):2006-2023. doi: 10.1002/sim.8885. Epub 2021 Jan 22.
Ovarian epithelial cancer is a gynecological tumor with a high risk of recurrence and death. In the clinical diagnosis of ovarian epithelial cancer, CA125 has become an important indicator of disease burden. To account for patient recurrence and death, a proper method is needed to integrate information from biomarkers and recurrence simultaneously. In the past 10 years, many methods have been proposed for joint modeling of longitudinal biomarkers and survival data, but few of them are applicable to longitudinal data and disease processes, including recurrence and death. In this article, we proposed a new joint frailty model based on functional principal component analysis for dynamic prediction of survival probabilities on the total time scale, which took recurrent history and longitudinal data into account simultaneously. The estimation of the joint frailty model is achieved by maximizing the penalized log-likelihood function. The simulation results demonstrated the advantages of our method in both discrimination and accuracy under different scenarios. To indicate the method's practicality, it is applied to an actual dataset of patients with ovarian epithelial cancer to predict survival dynamically using longitudinal data of biomarker CA125 and recurrent history data.
卵巢上皮癌是一种妇科肿瘤，具有较高的复发和死亡风险。在卵巢上皮癌的临床诊断中，CA125 已成为疾病负担的重要指标。为了考虑患者的复发和死亡，需要一种适当的方法来同时整合来自生物标志物和复发的信息。在过去的 10 年中，已经提出了许多用于联合建模纵向生物标志物和生存数据的方法，但很少有方法适用于包括复发和死亡在内的纵向数据和疾病过程。在本文中，我们提出了一种基于功能主成分分析的新的联合脆弱性模型，用于在总时间尺度上对生存概率进行动态预测，同时考虑了复发史和纵向数据。联合脆弱性模型的估计是通过最大化惩罚对数似然函数来实现的。模拟结果表明，在不同情况下，我们的方法在判别和准确性方面都具有优势。为了说明该方法的实用性，我们将其应用于卵巢上皮癌患者的实际数据集，使用生物标志物 CA125 的纵向数据和复发史数据来动态预测生存。
