Yang Zijing, Wu Hongji, Hou Yawen, Yuan Hao, Chen Zheng
Department of Biostatistics, School of Public Health (Guangdong Provincial Key Laboratory of Tropical Disease Research), Southern Medical University, Guangzhou, P.R.China.
Department of Statistics, Jinan University, Guangzhou, P.R.China.
Comput Methods Programs Biomed. 2021 Aug;207:106155. doi: 10.1016/j.cmpb.2021.106155. Epub 2021 May 9.
In the process of clinical diagnosis and treatment, the restricted mean survival time (RMST), which reflects the life expectancy of patients up to a specified time, can be used as an appropriate outcome measure. However, the RMST only calculates the mean survival time of patients within a period of time after the start of follow-up and may not accurately portray the change in a patient's life expectancy over time.
The life expectancy can be adjusted for the time the patient has already survived and defined as the conditional restricted mean survival time (cRMST). A dynamic RMST model based on the cRMST can be established by incorporating time-dependent covariates and covariates with time-varying effects. We analyzed data from a study of primary biliary cirrhosis (PBC) to illustrate the use of the dynamic RMST model, and a simulation study was designed to test the advantages of the proposed approach. The predictive performance was evaluated using the C-index and the prediction error.
Considering both the example results and the simulation results, the proposed dynamic RMST model, which can explore the dynamic effects of prognostic factors on survival time, has better predictive performance than the RMST model. Three PBC patient examples were used to illustrate how the predicted cRMST changed at different prediction times during follow-up.
The use of the dynamic RMST model based on the cRMST allows for the optimization of evidence-based decision-making by updating personalized dynamic life expectancy for patients.
在临床诊断和治疗过程中，受限平均生存时间（RMST）可作为一种合适的结局指标，它反映了患者到特定时间的预期寿命。然而，RMST仅计算随访开始后一段时间内患者的平均生存时间，可能无法准确描绘患者预期寿命随时间的变化。
预期寿命可根据患者已存活的时间进行调整，并定义为条件受限平均生存时间（cRMST）。通过纳入时间相依协变量和具有时变效应的协变量，可以建立基于cRMST的动态RMST模型。我们分析了一项原发性胆汁性肝硬化（PBC）研究的数据，以说明动态RMST模型的应用，并设计了一项模拟研究来检验所提出方法的优势。使用C指数和预测误差评估预测性能。
综合实例结果和模拟结果，所提出的动态RMST模型能够探索预后因素对生存时间的动态影响，其预测性能优于RMST模型。使用三个PBC患者实例来说明随访期间不同预测时间的预测cRMST如何变化。
基于cRMST使用动态RMST模型，通过更新患者的个性化动态预期寿命，有助于优化基于证据的决策。
