Variable selection based on A-learning for optimal treatment regimes with survival data under semiparametric model

In this study, we focus on high-dimensional time-to-event data in the situation of multiple treatment options. Employing the A-learning framework, we formulate A-learning-based estimating equations and introduce a loss function that can be integrated with regularization penalties. When either the baseline covariate model or the propensity score model is correctly specified, we achieve variable selection for key covariates to optimize treatment decision regimes. Extensive simulation experiments, along with an analysis of the ACTG 175 clinical trial involving HIV-infected patients, validate the effectiveness of the proposed methodology.