Debiased inference for heterogeneous subpopulations in a high-dimensional logistic regression model.

ABSTRACT: Due to the prevalence of complex data, data heterogeneity is often observed in contemporary scientific studies and various applications. Motivated by studies on cancer cell lines, we consider the analysis of heterogeneous subpopulations with binary responses and high-dimensional covariates. In many practical scenarios, it is common to use a single regression model for the entire data set. To do this effectively, it is critical to quantify the heterogeneity of the effect of covariates across subpopulations through appropriate statistical inference. However, the high dimensionality and discrete nature of the data can lead to challenges in inference. Therefore, we propose a novel statistical inference method for a high-dimensional logistic regression model that accounts for heterogeneous subp

SUBMITTER: Kim H 

PROVIDER: S-EPMC10713553 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature