Variable selection with broken adaptive ridge regression for interval-censored competing risks data

Competing risks data refer to situations in which the occurrence of one event precludes the occurrence of other events, resulting in multiple mutually exclusive events. This data type is commonly encountered in medical research and clinical trials, where it is used to explore the interplay among events and to inform decision-making in fields such as healthcare and epidemiology. We develop a penalized variable selection procedure to handle such complex data in an interval-censored setting. We consider a broad class of semiparametric transformation regression models, 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 simultaneously 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.