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• W2989746476 abstract "Current status data abounds in the field of epidemiology and public health, where the only observable data for a subject is the random inspection time and the event status at inspection. Motivated by such a current status data from a periodontal study where data are inherently clustered, we propose a unified methodology to analyze such complex data. We allow the time-to-event to follow the semiparametric GOR model with a cure fraction, and develop a unified estimation scheme powered by the EM algorithm. The within-subject correlation is accounted for by a random (frailty) effect, and the non-parametric component of the GOR model is approximated via penalized splines, with a set of knot points that increases with the sample size. Proposed methodology is accompanied by a rigorous asymptotic theory, and the related semiparametric efficiency. The finite sample performance of our model parameters are assessed via simulation studies. Furthermore, the proposed methodology is illustrated via application to the oral health data, accompanied by diagnostic checks to identify influential observations. An easy to use R package CRFCSD is also available for implementation." @default.
• W2989746476 created "2019-12-05" @default.
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• W2989746476 date "2019-11-30" @default.
• W2989746476 modified "2023-10-16" @default.
• W2989746476 title "Efficient Estimation of Mixture Cure Frailty Model for Clustered Current Status Data" @default.
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• W2989746476 doi "https://doi.org/10.48550/arxiv.1912.00295" @default.
• W2989746476 hasPublicationYear "2019" @default.
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