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
Volume108
Issue503
Date Published2013 09 01
ISSN0162-1459
Abstract

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.

DOI10.1080/01621459.2013.795487
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
Project: