Abstract

Predicting time-to-death for patients is one of the most important issues in survival analysis. A dynamic prediction method using a bivariate failure time model allows one to build a prediction formula based on tumor progression status observed during the follow-up. However, the existing spline models for the baseline hazard functions are not convenient for predicting long-term survival probability exceeding the largest follow-up time. Therefore, we proposed a parametric method based on the Weibull model to achieve long-term prediction. The present study aims to develop a prediction formula based on a Weibull-based bivariate failure time model, which is designed for individual patient data meta-analysis. We also consider prediction of residual life expectancy that is not possible by the nonparametric models. We conducted Monte Carlo simulations to compare the performance of the proposed model with the spline model. In addition, we illustrate the proposed methods through the analysis of breast cancer patients.

摘要

预测患者的死亡时间是生存分析中最重要的问题之一。一种使用双变量失效时间模型的动态预测方法，可以根据随访期间观察到的肿瘤进展状况建立预测公式。但是，现有的用于基线危害函数的样条模型不便于预测超过最大随访时间的长期生存率。因此，我们提出了一种基于威布尔模型的参数化方法来实现长期预测。本研究旨在开发基于基于Weibull的双变量失效时间模型的预测公式，该模型用于个人患者数据的荟萃分析。我们还考虑了非参数模型无法预测的预期剩余寿命。我们进行了蒙特卡洛模拟，以将提出的模型与样条模型的性能进行比较。此外，我们通过对乳腺癌患者的分析来说明所提出的方法。