标题： 区间审查竞争风险数据的断裂自适应岭回归变量选择 显示英文标题

标题： Variable Selection with Broken Adaptive Ridge Regression for Interval-Censored Competing Risks Data

摘要： 竞争风险数据指的是当一个事件的发生排除了其他事件发生的可能性，导致多个互斥事件的情况。 这种数据类型在医学研究和临床试验中经常遇到，用于探索不同事件之间的相互作用，并为医疗和流行病学等领域的决策提供依据。 我们开发了一种惩罚变量选择过程，以在区间删失的情况下处理此类复杂数据。 我们考虑了一类广泛的半参数变换回归模型，包括比例风险和非比例风险模型等流行模型。 为了促进稀疏性和选择与每个事件相关的变量，我们采用了断裂自适应岭（BAR）惩罚。 这种方法使我们能够同时选择重要的风险因素并估计每个研究事件的效应。 我们建立了BAR过程的Oracle性质，并通过模拟研究评估其性能。 所提出的方法被应用于一个真实的HIV队列数据集，进一步验证了其在实际中的适用性。 显示英文摘要

摘要： Competing risks data refer to situations where the occurrence of one event pre- cludes the possibility of other events happening, resulting in multiple mutually exclusive events. This data type is commonly encountered in medical research and clinical trials, exploring the interplay between different events and informing decision-making in fields such as healthcare and epidemiology. We develop a penal- ized variable selection procedure to handle such complex data in an interval-censored setting. We consider a broad class of semiparametric transformation regression mod- els, including popular models such as proportional and non-proportional hazards models. To promote sparsity and select variables specific to each event, we employ the broken adaptive ridge (BAR) penalty. This approach allows us to simultane- ously select important risk factors and estimate their effects for each event under investigation. We establish the oracle property of the BAR procedure and evaluate its performance through simulation studies. The proposed method is applied to a real-life HIV cohort dataset, further validating its applicability in practice.