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In an infectious disease cohort study, individuals who have been infected with a pathogen are often recruited for follow up. The period between infection and the onset of symptomatic disease, referred to as the incubation period, is of interest because of its importance on disease surveillance and control. However, the incubation period is often difficult to ascertain due to the uncertainty associated with asymptomatic infection onset time. An additional complication is that the observed infected subjects are likely to have longer incubation periods due to the prevalent sampling. In this article, we demonstrate how to estimate the distribution of the incubation period with the uncertain infection onset, subject to left-truncation and right-censoring. We employ a family of sufficiently general parametric models, the generalized odds-rate class of regression models, for the underlying incubation period and its correlation with covariates. In simulation studies, we assess the finite sample performance of the model fitting and hazard function estimation. The proposed method is illustrated on data from the HIV/AIDS study on injection drug users admitted to a detoxification program in Badalona, Spain. [ABSTRACT FROM AUTHOR]

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]