Risk of Tooth Loss After Cigarette Smoking Cessation

INTRODUCTION. Little is known about the effect of cigarette smoking cessation on risk of tooth loss. We examined how risk of tooth loss changed with longer periods of smoking abstinence in a prospective study of oral health in men. METHODS. Research subjects were 789 men who participated in the Veterans Administration Dental Longitudinal Study from 1968 to 2004. Tooth status and smoking status were determined at examinations performed every 3 years, for a maximum follow-up time of 35 years. Risk of tooth loss subsequent to smoking cessation was assessed sequentially at 1-year intervals with multivariate proportional hazards regression models. Men who never smoked cigarettes, cigars, or pipes formed the reference group. Hazard ratios were adjusted for age, education, total pack-years of cigarette exposure, frequency of brushing, and use of floss. RESULTS. The hazard ratio for tooth loss was 2.1 (95% confidence interval [CI], 1.5-3.1) among men who smoked cigarettes during all or part of follow-up. Risk of tooth loss among men who quit smoking declined as time after smoking cessation increased, from 2.0 (95% CI, 1.4-2.9) after 1 year of abstinence to 1.0 (95% CI, 0.5-2.2) after 15 years of abstinence. The risk remained significantly elevated for the first 9 years of abstinence but eventually dropped to the level of men who never smoked after 13 or more years. CONCLUSION. These results indicate that smoking cessation is beneficial for tooth retention, but long-term abstinence is required to reduce the risk to the level of people who have never smoked.U.S. Department of Veterans Affairs Epidemiology (Merit Review grant); Massachusetts Veterans Epidemiology Research and Information Center; National Institutes of Health (R01 DA10073, R03 DE016357, R15 DE12644, K24 DE00419

Multivariate Exponential Survival Trees And Their Application To Tooth Prognosis

This paper is concerned with developing rules for assignment of tooth prognosis based on actual tooth loss in the VA Dental Longitudinal Study. It is also of interest to rank the relative importance of various clinical factors for tooth loss. A multivariate survival tree procedure is proposed. The procedure is built on a parametric exponential frailty model, which leads to greater computational efficiency. We adopted the goodness-of-split pruning algorithm of [LeBlanc, M., Crowley, J., 1993. Survival trees by goodness of split. Journal of the American Statistical Association 88, 457-467] to determine the best tree size. In addition, the variable importance method is extended to trees grown by goodness-of-fit using an algorithm similar to the random forest procedure in [Breiman, L., 2001. Random forests. Machine Learning 45, 5-32]. Simulation studies for assessing the proposed tree and variable importance methods are presented. To limit the final number of meaningful prognostic groups, an amalgamation algorithm is employed to merge terminal nodes that are homogeneous in tooth survival. The resulting prognosis rules and variable importance rankings seem to offer simple yet clear and insightful interpretations. © 2008 Elsevier B.V. All rights reserved

Multivariate exponential survival trees and their application to tooth prognosis

This paper is concerned with developing rules for assignment of tooth prognosis based on actual tooth loss in the VA Dental Longitudinal Study. It is also of interest to rank the relative importance of various clinical factors for tooth loss. A multivariate survival tree procedure is proposed. The procedure is built on a parametric exponential frailty model, which leads to greater computational efficiency. We adopted the goodness-of-split pruning algorithm of [LeBlanc, M., Crowley, J., 1993. Survival trees by goodness of split. Journal of the American Statistical Association 88, 457-467] to determine the best tree size. In addition, the variable importance method is extended to trees grown by goodness-of-fit using an algorithm similar to the random forest procedure in [Breiman, L., 2001. Random forests. Machine Learning 45, 5-32]. Simulation studies for assessing the proposed tree and variable importance methods are presented. To limit the final number of meaningful prognostic groups, an amalgamation algorithm is employed to merge terminal nodes that are homogeneous in tooth survival. The resulting prognosis rules and variable importance rankings seem to offer simple yet clear and insightful interpretations.

Bayesian Survival Trees for Clustered Observations, Applied to Tooth Prognosis

Tooth loss from periodontal disease or dental caries (decay) afflicts most adults over the course of their lives. Survival tree methods for correlated observations have shown potential for developing objective tooth prognosis systems; however, the current technology suffers either from prohibitive computational expense or unrealistic simplifying assumptions to overcome computational demands. In this article Bayesian tree methods are developed for correlated survival data, relying on a computationally feasible, yet flexible, frailty model with piecewise constant hazard function. Bayesian stochastic search methods, using a Laplace approximated marginal likelihood, are detailed for tree construction, and posterior ensemble averaged variable importance ranking and amalgamation procedures are developed. The proposed methods are used to assign each tooth from the Veteran Administration (VA) Dental Longitudinal Study to one of five prognosis categories and evaluate the effects of clinical factors and genetic polymorphisms in predicting tooth loss. The prognostic rules established may be used in clinical practice to optimize tooth retention and devise periodontal treatment plans

Trees For Correlated Survival Data By Goodness Of Split, With Applications To Tooth Prognosis

In this article the regression tree method is extended to correlated survival data and applied to the problem of developing objective prognostic classification rules in periodontal research. The robust logrank statistic is used as the splitting statistic to measure the between-node difference in survival, while adjusting for correlation among failure times from the same patient. The partition-based survival function estimator is shown to converge to the true conditional survival function. Tooth loss data from 100 periodontal patients (2,509 teeth) was analyzed using the proposed method. The goal is to assign each tooth to one of the five prognosis categories (good, fair, poor, questionable, or hopeless). After the best-sized tree was identified, an amalgamation procedure was used to form five prognostic groups. The prognostic rules established here may be used by periodontists, general dentists, and insurance companies in devising appropriate treatment plans for periodontal oatients. © 2006 American Statistical Association