Hierarchical False Discovery Rate Control for High-dimensional Survival Analysis with Interactions

Weijuan Liang; Qingzhao Zhang; Shuangge Ma

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arXiv · 2311.13767

Hierarchical False Discovery Rate Control for High-dimensional Survival Analysis with Interactions

Weijuan Liang·Qingzhao Zhang·Shuangge Ma

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Abstract

With the development of data collection techniques, analysis with a survival response and high-dimensional covariates has become routine. Here we consider an interaction model, which includes a set of low-dimensional covariates, a set of high-dimensional covariates, and their interactions. This model has been motivated by gene-environment (G-E) interaction analysis, where the E variables have a low dimension, and the G variables have a high dimension. For such a model, there has been extensive research on estimation and variable selection. Comparatively, inference studies with a valid false discovery rate (FDR) control have been very limited. The existing high-dimensional inference tools cannot be directly applied to interaction models, as interactions and main effects are not ``equal". In this article, for high-dimensional survival analysis with interactions, we model survival using the Accelerated Failure Time (AFT) model and adopt a ``weighted least squares + debiased Lasso'' approach for estimation and selection. A hierarchical FDR control approach is developed for inference and respect of the ``main effects, interactions'' hierarchy. { The asymptotic distribution properties of the debiased Lasso estimators} are rigorously established. Simulation demonstrates the satisfactory performance of the proposed approach, and the analysis of a breast cancer dataset further establishes its practical utility.

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BibTeX RIS

Weijuan Liang, Qingzhao Zhang, Shuangge Ma. 2023-11-23. Hierarchical False Discovery Rate Control for High-dimensional Survival Analysis with Interactions. https://arxiv.org/abs/2311.13767

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