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.
Ann Epidemiol. 2020 Apr;44:45-51. doi: 10.1016/j.annepidem.2020.01.009. Epub 2020 Mar 4.
Providing up-to-date information on patient prognosis is important in determining the optimal treatment strategies. The currently available prediction models, such as the Cox model, are limited to making predictions from baseline and do not consider the time-varying effects of covariates.
A total of 1501 cervical cancer patients from the Surveillance, Epidemiology, and End Results (SEER) database were included. We introduced three landmark dynamic prediction models (models 1-3) that explore the dynamic effects of prognostic factors to obtain 5-year dynamic survival rate predictions at different prediction times. The performances of these models were evaluated by Harrell's C-index and the Brier score using cross-validation.
Some variables did not meet the proportional hazards assumption, indicating that the constant hazard ratios were unreliable. Model 3, which showed the best performance for prediction, was selected as the final model. Significant time-varying effects were observed for age at diagnosis, International Federation of Gynecology and Obstetrics (FIGO) stage, lymph node metastasis, and histological subtypes. Three patients were as examples used to illustrate how the predicted probabilities change at different prediction times during follow-up.
Model 3 can effectively incorporate covariates with time-varying effects and update the probability of surviving an additional 5 years at different prediction times. The use of the landmark approach may provide evidence for clinical decision making by updating personalized information for patients.
提供有关患者预后的最新信息对于确定最佳治疗策略非常重要。目前可用的预测模型，如 Cox 模型，仅限于从基线进行预测，并且不考虑协变量的时变效应。
共纳入来自监测、流行病学和最终结果（SEER）数据库的 1501 例宫颈癌患者。我们引入了三种里程碑式的动态预测模型（模型 1-3），以探索预后因素的动态效应，从而在不同的预测时间获得 5 年的动态生存率预测。通过交叉验证，使用 Harrell 的 C 指数和 Brier 评分评估这些模型的性能。
一些变量不符合比例风险假设，这表明常数风险比不可靠。表现最佳的预测模型 3 被选为最终模型。诊断时的年龄、国际妇产科联合会（FIGO）分期、淋巴结转移和组织学亚型等变量均观察到显著的时变效应。以 3 名患者为例，说明了在随访期间不同预测时间下预测概率如何变化。
模型 3 可以有效地纳入具有时变效应的协变量，并在不同的预测时间更新额外存活 5 年的概率。使用里程碑方法可以通过更新患者的个性化信息为临床决策提供依据。
