用于稀疏和不规则响应轨迹的纵向随机森林
Longitudinal Random Forests for Sparse and Irregular Response Trajectories
- Department of Mathematics and Statistics, Binghamton University（数学与统计学系，宾夕法尼亚大学伯灵顿分校）
- Department of Mathematics, Illinois State University（数学系，伊利诺伊州立大学）
- Department of Biostatistics, The University of North Carolina at Chapel Hill（生物统计学系，北卡罗来纳大学教堂山分校）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对纵向研究中稀疏不规则响应轨迹问题，提出纵向随机森林（LRF）框架，利用树集成学习与自适应节点级轨迹估计，有五项方法贡献，模拟显示其性能优越，能解决五个重要临床问题。
AI中文摘要：
纵向研究常在稀疏、不规则且不等距的时间点收集数据。这种异质性常由个体协变量驱动，但现有方法限于标量端点值，忽略了潜在响应轨迹。我们提出一种新颖的纵向随机森林（LRF）框架，利用基于树的集成机器学习与自适应节点级纵向轨迹估计。LRF框架有五项方法贡献，包括捕捉个体响应轨迹、引入新的分裂准则、提供两种变体、给出协变量的综合解释、能预测和预报轨迹。模拟研究表明LRF性能优于多种竞争方法，其实际意义在于能解决五个重要临床问题。
英文摘要：
Longitudinal studies often collect data at sparse, irregular, and unequally spaced time points. Such heterogeneity is often driven by subject-specific covariates, yet existing methods have been restricted to a scalar endpoint value, completely neglecting the underlying response trajectories. We propose a novel Longitudinal Random Forest (LRF) framework that leverages tree-based ensemble machine learning with adaptive node-wise longitudinal trajectory estimation. The LRF framework makes five methodological contributions. it captures each subject's individual response trajectory while simultaneously accommodating within-node correlation, between-node heterogeneity, and nonlinear and interactive covariate effects. It introduces a novel trajectory-based splitting criterion that maximizes trajectory separation while incorporating a size-weighted penalty; it provides two variants, Principal Analysis by Conditional Expectation (LRF-PACE) and adaptive linear mixed-effects models (LRF-adaptiveLMM), which employ nonparametric and semiparametric node-wise smoothers, respectively, while learning covariate effects in a data-driven manner. It provides a comprehensive interpretation of covariates using both the classical trajectory-based permutation variable importance measure (PVIM) and a newly proposed finite-way interaction frequency count, and it not only predicts entire trajectories for new subjects but also forecasts future trajectories for existing subjects. Extensive simulation studies demonstrate that LRF achieves superior performance over several competing methods, even under severe sparsity. The practical significance of the LRF framework lies in its ability to address five important clinical questions.
