半竞争风险下基于脆弱性的病亡模型的惩罚估计
Penalized Estimation of Frailty-Based Illness-Death Models for Semi-Competing Risks
AI总结：
针对半竞争风险高维场景下模型选择与预测方法缺失的问题，提出结合非凸与结构化融合惩罚的脆弱性病亡多状态模型惩罚估计框架，证明了统计误差界，经模拟和产科妊娠数据验证有效。
摘要：
半竞争风险是生存分析中的一类场景：非终点事件的发生受终点事件是否发生的影响，但反之不成立。半竞争风险广泛存在于各类临床场景中，一个典型的新例子是妊娠并发症子痫前期，它仅可能在分娩这一“终点”事件发生前出现。考虑半竞争风险的模型可用于探究协变量与结局联合发生时间之间的关联，但目前缺乏高维下半竞争风险的模型选择与预测方法。研究人员通常仅分析单一结局或复合结局，这会丢失有价值的信息并限制临床实用性——在产科场景中，这意味着忽略了子痫前期发病后分娩时机的重要洞见。为填补这一空白，我们提出了一种新颖的惩罚估计框架，用于半竞争风险的基于脆弱性的病亡多状态建模。我们的方法结合了非凸惩罚与结构化融合惩罚，既能实现全局稀疏性，又能保证各子模型的简洁性。我们通过非凸优化的路径式流程完成估计与模型选择，并证明了该场景下首个统计误差界结果。我们开展了模拟研究以评估估计误差与模型选择性能，还利用电子健康记录中的妊娠数据，将该方法全面应用于子痫前期与分娩时机的联合风险建模。
英文摘要：
Semi-competing risks refers to the survival analysis setting where the occurrence of a non-terminal event is subject to whether a terminal event has occurred, but not vice versa. Semi-competing risks arise in a broad range of clinical contexts, with a novel example being the pregnancy condition preeclampsia, which can only occur before the `terminal' event of giving birth. Models that acknowledge semi-competing risks enable investigation of relationships between covariates and the joint timing of the outcomes, but methods for model selection and prediction of semi-competing risks in high dimensions are lacking. Instead, researchers commonly analyze only a single or composite outcome, losing valuable information and limiting clinical utility -- in the obstetric setting, this means ignoring valuable insight into timing of delivery after preeclampsia has onset. To address this gap we propose a novel penalized estimation framework for frailty-based illness-death multi-state modeling of semi-competing risks. Our approach combines non-convex and structured fusion penalization, inducing global sparsity as well as parsimony across submodels. We perform estimation and model selection via a pathwise routine for non-convex optimization, and prove the first statistical error bound results in this setting. We present a simulation study investigating estimation error and model selection performance, and a comprehensive application of the method to joint risk modeling of preeclampsia and timing of delivery using pregnancy data from an electronic health record.
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