Smoothed ANOVA with spatial effects as a competitor to MCAR in multivariate spatial smoothing

TL;DR

This paper introduces a simplified spatial smoothing method called SANOVA as an alternative to the complex MCAR model for multivariate spatial data, demonstrating comparable performance in disease mapping applications.

Contribution

The authors extend SANOVA to incorporate spatial lattice effects and compare its effectiveness to MCAR, providing a more interpretable and easier-to-estimate model for multivariate spatial smoothing.

Findings

SANOVA performs comparably to MCAR in simulations.

SANOVA offers more interpretable covariance structures.

Application to cancer data shows SANOVA's competitiveness.

Abstract

Rapid developments in geographical information systems (GIS) continue to generate interest in analyzing complex spatial datasets. One area of activity is in creating smoothed disease maps to describe the geographic variation of disease and generate hypotheses for apparent differences in risk. With multiple diseases, a multivariate conditionally autoregressive (MCAR) model is often used to smooth across space while accounting for associations between the diseases. The MCAR, however, imposes complex covariance structures that are difficult to interpret and estimate. This article develops a much simpler alternative approach building upon the techniques of smoothed ANOVA (SANOVA). Instead of simply shrinking effects without any structure, here we use SANOVA to smooth spatial random effects by taking advantage of the spatial structure. We extend SANOVA to cases in which one factor is a spatial lattice, which is smoothed using a CAR model, and a second factor is, for example, type of cancer. Datasets routinely lack enough information to identify the additional structure of MCAR. SANOVA offers a simpler and more intelligible structure than the MCAR while performing as well. We demonstrate our approach with simulation studies designed to compare SANOVA with different design matrices versus MCAR with different priors. Subsequently a cancer-surveillance dataset, describing incidence of 3-cancers in Minnesota's 87 counties, is analyzed using both approaches, showing the competitiveness of the SANOVA approach.