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June 4, 2026Statistics and Its Interface0 citations

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

YFYuexin FangQLQian LiHLHongmei Li

Key Points

  • This research aims to optimize treatment decision regimes using variable selection based on the A-learning framework for survival data.
  • Utilized A-learning framework with estimating equations and a loss function integrated with regularization penalties.
  • Conducted extensive simulation experiments to validate the methodology.
  • Analyzed data from the ACTG 175 clinical trial involving HIV-infected patients.
  • Achieved effective variable selection when baseline covariate or propensity score models were correctly specified.
  • Showed improved treatment decision-making in high-dimensional time-to-event data.
  • Simulation results confirmed the performance of the method under various conditions.

Abstract

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.

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Cite This Study

Fang et al. (2026) studied this question.

synapsesocial.com/papers/6a211549d499ed480b16e82fhttps://doi.org/10.4310/sii.260601232001
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