FDR控制的广义线性模型与Cox回归的虚拟哑变量变量选择

中文摘要 AI 辅助

在基因组学、影像学和临床研究中，众多候选预测因子中通常只有少数与响应变量（可能是二元的、分类的、计数或删失事件时间）呈非线性关联。终止随机实验（T-Rex）选择器是一种可扩展的变量选择方法，通过让合成零变量（哑变量）与真实预测因子竞争来控制错误发现率（FDR）。虽然FDR控制理论适用于更一般的设置，但迄今为止，T-Rex选择器仅针对线性模型进行了具体化。我们通过将最近开发的虚拟哑变量构造扩展到基于评分的向前选择，针对伯努利、泊松、多项和Cox响应，提出了一种具有FDR控制的、内存高效的广义线性模型和Cox回归选择过程。基于虚拟哑变量的选择路径在分布上与显式增广保持等价，因此在相同假设下FDR控制得以延续。模拟实验证实了这种等价性以及正确模型设定所带来的功效提升。在模拟基因型和癌症生存数据上的应用展示了其现实适用性。

英文摘要

In genomics, imaging and clinical studies, only a few of many candidate predictors are often nonlinearly associated with a response that may be, e.g. binary, categorical, a count or a censored event time. The Terminating-Random Experiments (T-Rex) selector is a scalable variable selection method that controls the false discovery rate (FDR) by letting synthetic null variables (dummies) compete with the real predictors. While the FDR control theory embraces more general settings, to date, the T-Rex selector has been specified only for linear models. We propose a memory-efficient selection procedure with FDR control for generalized linear models and Cox regression by extending the recently developed virtual dummy construction to score-based forward selection for Bernoulli, Poisson, multinomial and Cox responses. The virtual-dummy-based selection path remains equal in distribution to explicit augmentation, so FDR control carries over under the same assumptions. Simulations confirm this equivalence and the power gained by correct model specification. Real-world applicability is illustrated on simulated genotypes and on cancer survival data.

发表机构

• TU Darmstadt（达姆施塔特工业大学）

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