A nonparametric spatial model for periodontal data with non-random missingness.

TitleA nonparametric spatial model for periodontal data with non-random missingness.
Publication TypeJournal Article
Year of Publication2013
AuthorsReich, Brian J., Dipankar Bandyopadhyay, and Howard D. Bondell
JournalJ Am Stat Assoc
Date Published2013 09 01

Periodontal disease progression is often quantified by clinical attachment level (CAL) defined as the distance down a tooth's root that is detached from the surrounding bone. Measured at 6 locations per tooth throughout the mouth (excluding the molars), it gives rise to a dependent data set-up. These data are often reduced to a one-number summary, such as the whole mouth average or the number of observations greater than a threshold, to be used as the response in a regression to identify important covariates related to the current state of a subject's periodontal health. Rather than a simple one-number summary, we set forward to analyze all available CAL data for each subject, exploiting the presence of spatial dependence, non-stationarity, and non-normality. Also, many subjects have a considerable proportion of missing teeth which cannot be considered missing at random because periodontal disease is the leading cause of adult tooth loss. Under a Bayesian paradigm, we propose a nonparametric flexible spatial (joint) model of observed CAL and the location of missing tooth via kernel convolution methods, incorporating the aforementioned features of CAL data under a unified framework. Application of this methodology to a data set recording the periodontal health of an African-American population, as well as simulation studies reveal the gain in model fit and inference, and provides a new perspective into unraveling covariate-response relationships in presence of complexities posed by these data.

Alternate JournalJ Am Stat Assoc
Original PublicationA nonparametric spatial model for periodontal data with non-random missingness.
PubMed ID24288421
PubMed Central IDPMC3839869
Grant ListR01 MH084022 / MH / NIMH NIH HHS / United States
R01 ES014843 / ES / NIEHS NIH HHS / United States
R03 DE020114 / DE / NIDCR NIH HHS / United States
R03 DE021762 / DE / NIDCR NIH HHS / United States
P01 CA142538 / CA / NCI NIH HHS / United States