Favoring the hierarchical constraint in penalized survival models for randomized trials in precision medicine.

ABSTRACT:

Background

The research of biomarker-treatment interactions is commonly investigated in randomized clinical trials (RCT) for improving medicine precision. The hierarchical interaction constraint states that an interaction should only be in a model if its main effects are also in the model. However, this constraint is not guaranteed in the standard penalized statistical approaches. We aimed to find a compromise for high-dimensional data between the need for sparse model selection and the need for the hierarchical constraint.

Results

To favor the property of the hierarchical interaction constraint, we proposed to create groups composed of the biomarker main effect and its interaction with treatment and to perform the bi-level selection on these groups. We proposed two weightin

SUBMITTER: Belhechmi S 

PROVIDER: S-EPMC10022294 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature