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We propose a penalized variable selection method for the Cox proportional hazards model with interval censored data. It conducts a penalized nonparametric maximum likelihood estimation with an adaptive lasso penalty, which can be implemented through a penalized EM algorithm. The method is proven to enjoy the desirable oracle property. We also extend the method to left truncated and interval censored data. Our simulation studies show that the method possesses the oracle property in samples of modest sizes and outperforms available existing approaches in many of the operating characteristics. An application to a dental caries data set illustrates the method's utility. [ABSTRACT FROM AUTHOR]

We propose a semiparametric multi-state frailty model to analyze clustered event-history data subject to interval censoring. The proposed model is motivated by an attempt to study the life course of dental caries at the tooth level, taking into account the multiplicity of caries states and the intra-oral clustering of observations made at periodic time points. Of particular interest is the study of the intra-oral distribution of processes leading to carious lesions, and whether this distribution varies with gender. The model assumes, in view of the covariate profile, a proportionality of the transition intensities conditional on subject-level frailties, coupled with a linear spline approximation of the log baseline intensities. The model estimation is conducted using a penalized likelihood where the smoothing parameters are estimated as reciprocal variance components under a mixed-model representation. A Bayesian method is proposed to predict tooth-level caries transition probabilities, which can be used for tailoring tooth-level caries treatment and prevention plans. Intensive simulation studies indicate that the model fitting and prediction perform reasonably well under realistic sample sizes. The practical utility of the methods is illustrated using data from a longitudinal study on oral health among children from low-income families residing in the city of Detroit, Michigan. [ABSTRACT FROM AUTHOR]

We propose a three-state frailty Markov model coupled with likelihood-based inference to analyse tooth level life course data in caries research. This analysis is challenging because of intraoral clustering, interval censoring, multiplicity of caries states and computational complexities. We also develop a Bayesian approach to predict future caries transition probabilities given observed life history data. Numerical experiments demonstrate that the methods proposed perform very well in finite samples with moderate sizes. The practical utility of the model is illustrated by using life course data from a unique longitudinal study of dental caries in young low income urban African-American children. In this analysis, we evaluate for any spatial symmetry in the mouth with respect to the life course of dental caries, and whether the same type of tooth has a similar decay process in boys and girls. [ABSTRACT FROM AUTHOR]