Exploring tooth survival using Bayesian spatial models Caries and severe periodontal disease eventually lead to tooth loss, and this remains a major public health burden in the US. Future dental treatment plans will benefit from development of advanced statistical methods to integrate efficient risk assessment and short-term prediction of tooth loss. Dental datasets come with many interesting statistical challenges which severely limit the potential of currently available methods. In addition to tooth-within-mouth clustering, the times to events are spatially dependent, non-stationary (varying with tooth-locations), and experience heavy censoring. These factors also complicate the interpretation of clinical findings, which are needed at the conditional (subject-level) and the marginal (population) levels. Currently available statistical methods might handle some, but not all of these within an unified paradigm. Goals: Using a Bayesian framework, the proposed study will assess and monitor dental disease status of a population of interest and identify covariates associated with tooth- loss leading to efficient short-term prediction. Subjects: The statistical methods will be initially evaluated on a dataset of about 100 dentate subjects from the McGuire and Nunn data who were monitored at a private dental practice in the Houston area for about 16 years. For generalizability, the methods will be tested on a 4-year longitudinal database consisting of about 16,500 patients collected at Creighton University. Study design: A clustered-longitudinal study design with time to event endpoint comprises the databases that recorded age, gender, race, complete restorative and periodontal records with follow-up, smoking status, diabetes status, oral hygiene, and other essential parameters. Significance: The current project will provide new knowledge to unravel the complex covariate-response relationship that determines tooth loss, and can be easily generalized to other dental datasets. The long-term goal is to be able to achieve accurate predictive inference on tooth survival enabling dental practitioners to develop cost-effective dental treatment plans.
This proposed research has the potential to exert tremendous impact from a dental public health perspective because it combines the various complexities of available dental time-to-event data within a unified paradigm to study complex covariate-response relationships determining tooth-loss. The long term goal is to bridge the gap between academic dental basic science researchers and practitioners who can rely on accurate predictive inference on tooth survival to develop cost-effective treatment plans.
| Chernoukhov, A; Hussein, A; Nkurunziza, S et al. (2018) Bayesian inference in time-varying additive hazards models with applications to disease mapping. Environmetrics 29: |
| Zhao, Weihua; Lian, Heng; Bandyopadhyay, Dipankar (2018) A partially linear additive model for clustered proportion data. Stat Med 37:1009-1030 |
| Wu, Xiaowei; Guan, Ting; Liu, Dajiang J et al. (2018) ADAPTIVE-WEIGHT BURDEN TEST FOR ASSOCIATIONS BETWEEN QUANTITATIVE TRAITS AND GENOTYPE DATA WITH COMPLEX CORRELATIONS. Ann Appl Stat 12:1558-1582 |
| Lewis, Bradley R; Bandyopadhyay, Dipankar; DeSantis, Stacia M et al. (2017) Augmenting beta regression for periodontal proportion data via the SAS NLMIXED procedure. J Appl Probab Stat 12:49-66 |
| Lan, Ling; Bandyopadhyay, Dipankar; Datta, Somnath (2017) Non-parametric regression in clustered multistate current status data with informative cluster size. Stat Neerl 71:31-57 |
| Galarza, Christian E; Lachos, Victor H; Bandyopadhyay, Dipankar (2017) Quantile regression in linear mixed models: a stochastic approximation EM approach. Stat Interface 10:471-482 |
| Cai, Bo; Bandyopadhyay, Dipankar (2017) Bayesian semiparametric variable selection with applications to periodontal data. Stat Med 36:2251-2264 |
| Bandyopadhyay, Dipankar; Galvis, Diana M; Lachos, Victor H (2017) Augmented mixed models for clustered proportion data. Stat Methods Med Res 26:880-897 |
| Bandyopadhyay, Dipankar; Jácome, M Amalia (2016) Comparing conditional survival functions with missing population marks in a competing risks model. Comput Stat Data Anal 95:150-160 |
| Bandyopadhyay, Dipankar; Canale, Antonio (2016) Nonparametric spatial models for clustered ordered periodontal data. J R Stat Soc Ser C Appl Stat 65:619-640 |
Showing the most recent 10 out of 13 publications
