Individual Data Protected Integrative Regression Analysis of High-Dimensional Heterogeneous Data.

ABSTRACT: Evidence-based decision making often relies on meta-analyzing multiple studies, which enables more precise estimation and investigation of generalizability. Integrative analysis of multiple heterogeneous studies is, however, highly challenging in the ultra high-dimensional setting. The challenge is even more pronounced when the individual-level data cannot be shared across studies, known as DataSHIELD contraint. Under sparse regression models that are assumed to be similar yet not identical across studies, we propose in this paper a novel integrative estimation procedure for data-Shielding High-dimensional Integrative Regression (SHIR). SHIR protects individual data through summary-statistics-based integrating procedure, accommodates between-study heterogeneity in both the covariate distri

SUBMITTER: Cai T 

PROVIDER: S-EPMC10653033 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature