Marginally interpretable spatial logistic regression with bridge processes

TL;DR

This paper introduces a novel spatial logistic regression model that preserves both population-averaged and subject-specific interpretations by using bridge processes for spatial random effects, enhancing prediction and interpretability.

Contribution

The paper proposes a new class of spatial logistic regression models utilizing bridge processes, offering a full probabilistic characterization while maintaining interpretability.

Findings

Model has appealing computational properties

Demonstrated effective on childhood malaria data

Provides both population-averaged and subject-specific insights

Abstract

In including random effects to account for dependent observations, the odds ratio interpretation of logistic regression coefficients is changed from population-averaged to subject-specific. This is unappealing in many applications, motivating a rich literature on methods that maintain the marginal logistic regression structure without random effects, such as generalized estimating equations. However, for spatial data, random effect approaches are appealing in providing a full probabilistic characterization of the data that can be used for prediction. We propose a new class of spatial logistic regression models that maintain both population-averaged and subject-specific interpretations through a novel class of bridge processes for spatial random effects. These processes are shown to have appealing computational and theoretical properties, including a scale mixture of normal representation. The new methodology is illustrated with simulations and an analysis of childhood malaria prevalence data in the Gambia.