A latent factor model for spatial data with informative missingness

TL;DR

This paper introduces a multivariate spatial model that jointly analyzes binary, continuous, and missing data in periodontal exams, improving understanding of periodontal health by leveraging spatial correlations.

Contribution

It develops a novel latent spatial framework that simultaneously models responses and missingness, addressing challenges of mixed data types and informative missingness in periodontal data.

Findings

Joint modeling improves estimation accuracy.

Exploiting spatial correlations enhances inference.

Model performs well on simulated and real data.

Abstract

A large amount of data is typically collected during a periodontal exam. Analyzing these data poses several challenges. Several types of measurements are taken at many locations throughout the mouth. These spatially-referenced data are a mix of binary and continuous responses, making joint modeling difficult. Also, most patients have missing teeth. Periodontal disease is a leading cause of tooth loss, so it is likely that the number and location of missing teeth informs about the patient's periodontal health. In this paper we develop a multivariate spatial framework for these data which jointly models the binary and continuous responses as a function of a single latent spatial process representing general periodontal health. We also use the latent spatial process to model the location of missing teeth. We show using simulated and real data that exploiting spatial associations and jointly modeling the responses and locations of missing teeth mitigates the problems presented by these data.