Unpaired Data Empowers Association Tests

Abstract

To achieve a holistic view of the underlying mechanisms of human diseases, the biomedical research community is moving toward harvesting retrospective data available in Electronic Healthcare Records (EHRs). The first step for causal understanding is to perform association tests between types of potentially high-dimensional biomedical data, such as genetic, blood biomarkers, and imaging data. To obtain a reasonable power, current methods require a substantial sample size of individuals with both data modalities. This prevents researchers from using much larger EHR samples that include individuals with at least one data type, limits the power of the association test, and may result in higher false discovery rate. We present a new method called the Semi-paired Association Test (SAT) that makes use of both paired and unpaired data. In contrast to classical approaches, incorporating unpaired data allows SAT to produce better control of false discovery and, under some conditions, improve the association test power. We study the properties of SAT theoretically and empirically, through simulations and application to real studies in the context of Chronic Obstructive Pulmonary Disease. Our method identifies an association between the high-dimensional characterization of Computed Tomography (CT) chest images and blood biomarkers as well as the expression of dozens of genes involved in the immune system.

Graphical modeling of causal factors associated with the postoperative survival of esophageal cancer subjects

“…In contrast, causal discovery enables the visualization of the underlying causal relationship among data variables in a straightforward manner, providing a clear explanation based on the graph topology. The use of causal language allows for the decomposition of the domain adaptation problem into distinctive situations that may require different approaches 45 . However, it is important to note that while methods like GGES or GES excel at identifying the underlying causal relationships between variables, they may not perform as well in prediction compared to dedicated machine learning methods.…”