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

A latent factor model for spatial data with informative missingness