含时间依赖性治疗变量预后预测模型构建策略的模拟研究

Modeling strategies for prognostic models with time-dependent treatment variables

目的探究构建临床预后预测模型中时间依赖性变量的处理方法,将边际结构模型与临床预后预测模型结合,为患者的个体化预后评估提供更精确的预测工具。方法通过数据模拟,构建样本量为7000的训练数据集、样本量为3000的验证数据集,设置在不同随访时间及不同情境下,分别比较忽略治疗模型、基线无治疗模型、基线治疗预测模型和边际结构预测模型的预测效能。结果模拟研究设置2种随访时间点情况(2个、5个时间点),64种不同的治疗变量、时依性协变量、结局变量三者之间关系,共128种模拟情境。2个随访时间点情况下,边际结构预测模型预测效能与基线治疗预测模型预测效能未见明显差别,但均高于忽略治疗模型和基线无治疗模型;5个随访时间点情况下,边际结构预测模型预测效能优于其余三个预测模型。结论观察性队列存在时间依赖性治疗情况,在构建临床预后预测模型时,应该考虑基线后治疗发生变化的情况,否则会降低预后预测模型的预测效能。

Objective To explore the method of constructing time-dependent variables of clinical prognostic model,and to combine marginal structure model with clinical prognostic model to provide a more accurate tool for individualized prognostic assessment of patients.Methods Through data simulation,a training dataset with sample size of 7000 and a validation dataset with sample size of 3000 were constructed,and the predictive efficacy of ignoring treatment model,baseline no-treatment model,baseline treatment prediction model and marginal structure prediction model were respectively compared under different follow-up times and different situations.Results At 2 follow-up time points,there was no significant difference between the marginal structure prediction model and the baseline treatment prediction model,but they were higher than the neglected treatment model and the baseline no treatment model.At 5 follow-up time points,the prediction ability of the marginal structure prediction model was significantly higher than that of the other three prediction models.Conclusion In the case of time-dependent treatment in the observational cohort,the change of treatment after baseline should be considered when constructing the clinical prognosis model,otherwise the prediction accuracy of the prognosis model will be reduced.