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Adaptive Multi-Prior Lasso for High-Dimensional Generalized Linear Models

高维广义线性模型的自适应多先验 Lasso

Incorporation of external information into high-dimensional modeling for gene expression data has been shown, both theoretically and empirically, to substantially enhance performance. Such external information, sometimes referred to as prior information or priors, has become increasingly accessible from multiple sources, yet its reliability may vary considerably. Existing approaches often integrate these priors without sufficiently accounting for their quality, which may…

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Doubly robust estimators of the restricted mean time in favor estimands in individual- and cluster-randomized trials

个体与整群随机试验中治疗占优限制平均时间的双重稳健估计

Progressive multi-state survival outcomes are common in trials with recurrent or sequential events and require treatment effect estimands that remain interpretable without proportional intensity or Markov assumptions. The restricted mean time in favor of treatment (RMT-IF) extends the restricted mean survival time to ordered multi-state processes and provides such an interpretable estimand. However, existing RMT-IF methods are nonparametric, assume covariate-independent…

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