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Reich, Brian J.; Bandyopadhyay, Dipankar

A latent factor model for spatial data with informative missingness. (English) Zbl 1189.62175

Ann. Appl. Stat. 4, No. 1, 439-459 (2010).
Summary: 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.
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.

Cited in 18 Documents

MSC:

62P10 Applications of statistics to biology and medical sciences; meta analysis
92C50 Medical applications (general)
62H11 Directional data; spatial statistics
65C60 Computational problems in statistics (MSC2010)
62M30 Inference from spatial processes

Keywords:

binary spatial data; informative cluster size; multivariate data; periodontal data; probit regression; shared parameter model

Software:

spBayes
Cite Review PDF
Full Text: DOI arXiv

References:

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