Alleviating Spatial Confounding in Spatial Frailty Models

TL;DR

This paper proposes a novel two-step projection-based method to reduce spatial confounding in spatial frailty models, especially when fixed and spatial effects have different supports, using INLA for fast inference.

Contribution

It introduces a new approach to alleviate spatial confounding in spatial frailty models with differing supports of effects, enhancing modeling accuracy.

Findings

Method effectively reduces spatial confounding in case study.

Application to cancer data demonstrates improved model performance.

Fast inference achieved with INLA methodology.

Abstract

Spatial confounding is how is called the confounding between fixed and spatial random effects. It has been widely studied and it gained attention in the past years in the spatial statistics literature, as it may generate unexpected results in modeling. The projection-based approach, also known as restricted models, appears as a good alternative to overcome the spatial confounding in generalized linear mixed models. However, when the support of fixed effects is different from the spatial effect one, this approach can no longer be applied directly. In this work, we introduce a method to alleviate the spatial confounding for the spatial frailty models family. This class of models can incorporate spatially structured effects and it is usual to observe more than one sample unit per area which means that the support of fixed and spatial effects differs. In this case, we introduce a two folded projection-based approach projecting the design matrix to the dimension of the space and then projecting the random effect to the orthogonal space of the new design matrix. To provide fast inference in our analysis we employ the integrated nested Laplace approximation methodology. The method is illustrated with an application with lung and bronchus cancer in California - US that confirms that the methodology efficiency.