ArticleGenes & genomics2026
Parametric hypothesis testing for pathway based hierarchical structural component models.
Article in Genes & genomics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundHierarchical Structural Component Model (HisCoM) are statistical tools developed for pathway analysis that enable the simultaneous evaluation of multiple pathways within a single model. Traditionally, HisCoM utilize permutation tests to assess the significance of pathways in relation to a phenotype of interest. While permutation tests are effective for generating exact distributions under the null hypothesis when the asymptotic distribution is unknown, they are computationally intensive, particularly for high-dimensional datasets, as they require significant time to compute p-values.
objectiveThe aim of this study is to develop parametric testing procedures for HisCoM to determine pathway significance without relying on permutations. These parametric tests aim to improve statistical performance while significantly reducing computational burden.
methodsThe proposed parametric tests are built on asymptotic theory for high-dimensional frameworks with numerous biomarkers and pathways. Specifically, we introduce methods such as the standard [Formula: see text] asymptotic test, non-centrality test, degrees of freedom (df) adjustment, saddle point approximation, and modified asymptotic tests with full df and one df.
resultsSimulation and real data results showed substantial reductions in computational time compared to permutation-based test. Through the simulation study, the modified [Formula: see text] asymptotic test with full df shown to have higher power compare to the other methods. The real data analysis further supported its robustness and practical advantages.
conclusionThis study presents computationally efficient parametric testing methods for HisCoM. Based on simulation results and real data analysis, the modified [Formula: see text] asymptotic test with full df is recommended as the most reliable and effective approach for assessing pathway significance.
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