  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.
