De Iorio, M;
BAYESIAN SEMIPARAMETRIC INFERENCE FOR MULTIVARIATE DOUBLY-INTERVAL-CENSORED DATA.
ANN APPL STAT
2126 - 2149.
Based on a data set obtained in a dental longitudinal study, conducted in Flanders (Belgium), the joint time to caries distribution of permanent first molars was modeled as a function of covariates. This involves an analysis of multivariate continuous doubly-interval-censored data since: (i) the emergence time of a tooth and the time it experiences caries were recorded yearly, and (ii) events on teeth of the same child are dependent. To model the joint distribution of the emergence times and the times to caries, we propose a dependent Bayesian semiparametric model. A major feature of the proposed approach is that survival curves can be estimated without imposing assumptions such as proportional hazards, additive hazards, proportional odds or accelerated failure time.
|Title:||BAYESIAN SEMIPARAMETRIC INFERENCE FOR MULTIVARIATE DOUBLY-INTERVAL-CENSORED DATA|
|Keywords:||Multivariate doubly-interval-censored data, Bayesian nonparametrics, linear dependent Poisson-Dirichlet prior, linear dependent Dirichlet process prior, NONPARAMETRIC-ESTIMATION, DIRICHLET PROCESSES, AIDS, MODELS, CARIES, TIME, PRIORS|
|UCL classification:||UCL > School of BEAMS > Faculty of Maths and Physical Sciences > Statistical Science|
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