A0903
Title: Spatial confounding in spatial linear mixed models
Authors: Kori Khan - Iowa State University (United States) [presenting]
Abstract: In the last two decades, considerable research has been devoted to a phenomenon known as spatial confounding. Spatial confounding is thought to occur when there is collinearity between a covariate and the random effect in a spatial regression model. This collinearity is often considered highly problematic when the inferential goal is estimating regression coefficients and various methodologies have been proposed to "alleviate" it. Recently, it has become apparent that many of these methodologies are flawed, yet the field continues to expand. The purpose is to synthesise work in the field of spatial confounding. It is proposed that at least two distinct phenomena are currently conflated with the term spatial confounding. These are referred to as the analysis model and the data generation types of spatial confounding. In the context of spatial linear mixed models, it is shown that these two issues can lead to contradicting conclusions about whether spatial confounding exists and whether methods to alleviate it will improve inference. The results also illustrate that, in many cases, traditional spatial models help improve the inference of regression coefficients. Drawing on the insights gained, a path forward is offered for research in spatial confounding.
