适配多层数据的基于树的多重插补方法？一项模拟研究
Adapting tree-based multiple imputation methods for multi-level data? A simulation study
AI总结：
本研究针对多层数据插补问题，通过模拟实验评估了加入聚类虚拟变量的两种适配型树基插补方法与传统MICE的性能，发现高缺失率下适配的mixgb方法更具优势，为多层数据分析提供了新的有效插补选择。
摘要：
当数据具有层级结构（例如嵌套在教室中的学生）时，忽略观测值之间的依赖性会损害插补程序的有效性。标准的基于树的插补方法默认假设观测值之间相互独立，这限制了它们在多层数据场景中的适用性。尽管链式方程多元插补（MICE）被广泛用于处理层级数据，但它存在局限性，包括对模型设定的敏感性以及计算复杂度较高。替代性的基于树的方法已在个体层面数据中展现出应用前景，但在层级情境下仍 largely 未被探索。在这项模拟研究中，我们系统评估了两种新型基于树的方法——链式随机森林（Chained Random Forests）和极端梯度提升（mixgb）——的性能，这两种方法通过加入指示聚类归属的虚拟变量，被明确适配于多层数据。我们使用随机截距和随机斜率两种数据生成模型，在不同的聚类规模、缺失机制和缺失率下，从系数估计偏差、I类错误率和统计功效三个方面，将这些基于树的方法及其适配版本与传统的MICE插补进行了比较。结果表明，MICE能为第二层变量提供稳健且准确的推断，尤其是在缺失率较低的情况下。然而，在缺失率较高（30%、50%）时，适配后的提升方法（带聚类虚拟变量的mixgb）在第一层变量上的表现始终优于其他方法。对于第二层变量，尽管MICE在中等缺失率（30%）下仍保持更好的功效，但在高缺失率（50%）下，无论缺失机制或聚类规模如何，适配后的提升方法都更具优势。这些发现凸显了经过恰当适配的基于树的插补方法，在多层数据分析中作为传统MICE的有效替代方案的潜力。
英文摘要：
When data have a hierarchical structure, such as students nested within classrooms, ignoring dependencies between observations can compromise the validity of imputation procedures. Standard tree-based imputation methods implicitly assume independence between observations, limiting their applicability in multilevel data settings. Although Multivariate Imputation by Chained Equations (MICE) is widely used for hierarchical data, it has limitations, including sensitivity to model specification and computational complexity. Alternative tree-based approaches have shown promise for individual-level data, but remain largely unexplored for hierarchical contexts. In this simulation study, we systematically evaluate the performance of novel tree-based methods--Chained Random Forests and Extreme Gradient Boosting (mixgb)--explicitly adapted for multi-level data by incorporating dummy variables indicating cluster membership. We compare these tree-based methods and their adapted versions with traditional MICE imputation in terms of coefficient estimation bias, type I error rates and statistical power, under different cluster sizes, missingness mechanisms and missingness rates, using both random intercept and random slope data generation models. The results show that MICE provides robust and accurate inference for level 2 variables, especially at low missingness rates. However, the adapted boosting approach (mixgb with cluster dummies) consistently outperforms other methods for Level-1 variables at higher missingness rates (30%, 50%). For level 2 variables, while MICE retains better power at moderate missingness (30%), adapted boosting becomes superior at high missingness (50%), regardless of the missingness mechanism or cluster size. These findings highlight the potential of appropriately adapted tree-based imputation methods as effective alternatives to conventional MICE in multilevel data analyses.
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