Adaptive Elastic Net结合加速失效时间模型在亚组识别中的应用

Subgroup identification based on an accelerated failure time model combined with adaptive elastic net

目的针对临床试验中的生存数据,基于加速失效时间模型提出一种亚组识别方法。方法将Adaptive Elastic Net应用于加速失效时间模型(称为惩罚模型),通过检验协变量与治疗组别的交互项来识别亚组相关协变量。采用基于极大似然的change-point算法寻找预测计分的截断点以对患者进行亚组分类。采用二阶段适应性设计,以评价治疗效果是否存在于所识别的获益亚组人群中。对比四种模型(含协变量主效应的惩罚模型、单变量模型,以及不含协变量主效应的惩罚模型、单变量模型)的亚组识别效果。结果模拟结果显示,在样本量较小、删失率较高、获益亚组占比较小以及样本量不超过协变量个数的情况下,含协变量主效应的惩罚模型在获益亚组的识别上有明显的优势;而其他情况下,则是不含主效应的单变量模型较优。在二阶段适应性设计中,这两种模型进行亚组识别的Ⅰ类错误均控制在0.05左右;当潜在获益亚组时,相比于传统设计,适应性设计很大程度上提高了检验效能。结论含协变量主效应的惩罚模型适用于生存数据的亚组识别;相比于传统设计,二阶段适应性设计更适用于潜在获益亚组的疗效评价。

Objective We propose a strategy for identifying subgroups with the treatment effect from the survival data of a randomized clinical trial based on accelerated failure time(AFT)model.Methods We applied adaptive elastic net to the AFT model(designated as the penalized model)and identified the candidate covariates based on covariate-treatment interactions.To classify the patient subgroups,we utilized a likelihood-based change-point algorithm to determine the threshold cutoff point.A two-stage adaptive design was adopted to verify if the treatment effect existed within the identified subgroups.Results The penalized model with the main effect of the covariates considerably outperformed the univariate model without the main effect for the trial data with a small sample size,a high censoring rate,a small subgroup size,or a sample size that did not exceed the number of covariates;in other scenarios,the latter model showed better performances.Compared with the traditional design,the adaptive design improved the power for detecting the treatment effect where subgroup effect exists with a well-controlled type I error.Conclusion The penalized AFT model with the main effect of the covariates has advantages in subgroup identification from the survival data of clinical trials.Compared with the traditional design,the two-stage adaptive design has better performance in evaluation of the treatment effect when a subgroup effect exists.