High dimensional mediation analysis with latent variables.

ABSTRACT: We propose a model for high dimensional mediation analysis that includes latent variables. We describe our model in the context of an epidemiologic study for incident breast cancer with one exposure and a large number of biomarkers (i.e., potential mediators). We assume that the exposure directly influences a group of latent, or unmeasured, factors which are associated with both the outcome and a subset of the biomarkers. The biomarkers associated with the latent factors linking the exposure to the outcome are considered "mediators." We derive the likelihood for this model and develop an expectation-maximization algorithm to maximize an L1-penalized version of this likelihood to limit the number of factors and associated biomarkers. We show that the resulting estimates are consistent and t

SUBMITTER: Derkach A 

PROVIDER: S-EPMC8811931 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature