Pages that link to "Item:Q5280185"
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The following pages link to Identification of biomarker‐by‐treatment interactions in randomized clinical trials with survival outcomes and high‐dimensional spaces (Q5280185):
Displaying 11 items.
- Ranked sparsity: a cogent regularization framework for selecting and estimating feature interactions and polynomials (Q61016) (← links)
- Sparse classification with paired covariates (Q127641) (← links)
- A simulation study comparing different statistical approaches for the identification of predictive biomarkers (Q2003678) (← links)
- Oblique random survival forests (Q2281237) (← links)
- IPF-LASSO: integrative \(L_1\)-penalized regression with penalty factors for prediction based on multi-omics data (Q2405418) (← links)
- Identifying optimal biomarker combinations for treatment selection via a robust kernel method (Q3465367) (← links)
- A Scalable Hierarchical Lasso for Gene–Environment Interactions (Q5057242) (← links)
- Two‐stage penalized regression screening to detect biomarker–treatment interactions in randomized clinical trials (Q6055537) (← links)
- Efficient screening of predictive biomarkers for individual treatment selection (Q6094206) (← links)
- A three-stage approach to identify biomarker signatures for cancer genetic data with survival endpoints (Q6596729) (← links)
- Two-step hypothesis testing to detect gene-environment interactions in a genome-wide scan with a survival endpoint (Q6628047) (← links)
