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.

翻译：竞争风险数据指某一事件的发生会排除其他事件发生的可能性，从而形成多个互斥事件的情形。此类数据常见于医学研究与临床试验中，可用于探究不同事件间的相互作用，并为医疗健康与流行病学等领域的决策提供依据。本文针对区间删失背景下的此类复杂数据，提出一种惩罚变量选择方法。我们考虑一类广泛的半参数变换回归模型，包括比例风险模型与非比例风险模型等常用模型。为促进稀疏性并选择各事件特有的变量，我们采用断裂自适应岭（BAR）惩罚项。该方法能够同时筛选重要风险因素并估计其对所研究各事件的效应。我们建立了BAR方法的oracle性质，并通过模拟研究评估其性能。所提方法应用于真实HIV队列数据集，进一步验证了其实际适用性。