Frequentist and Bayesian approaches for interval-censored data
Interval censoring appears when the event of interest is only known to have occurred within a random time interval. Estimation and hypothesis testing procedures for interval-censored data are surveyed. We distinguish between frequentist and Bayesian approaches. Computational aspects for every proposed method are described and solutions with S-Plus, whenever are feasible, are mentioned. Three real data sets are analyzed.
Key WordsAIDS Bayesian inference Hypothesis testing Interval censoring Nonparametric methods Permutational tests
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- Best, N.G., Cowles, M.K. and Vines, S.K. (1995)CODA Manual version 0.30, MRC Biostatistics unit, Cambridge, UK.Google Scholar
- Courgeau, D. and Najim, J. (1996) Interval-censored event history analysis.Population: An English Selection,8, 191–298.Google Scholar
- Doss, H. and Narasimhan (1998) Dynamic diplay of changing posterior in Bayesian survival analysis, inPractical Nonparametric and Semiparametric Bayesian Statistics (eds. Dey, D. Müller, P. and Sinha, D.), New York: Springer-Verlag, 63–87.Google Scholar
- Gómez, G., Julià, O. and Utzet, F. (1992) Survival Analysis for Left Censored Data.Survival Analysis: State of the Art. Editors: J.P. Klein and P.K. Goel. Kluwer Academic Publishers. ISBN 0-7923-1634-7.Google Scholar
- Peto, R. (1973) Experimental survival curves for interval-censored Data.Journal of the Royal Statistical Society, Series C 22, 86–91.Google Scholar
- Rai, K., Susarla, V. and van Ryzin, J. (1980) Shrinkage estimation in nonparametric Bayesian survival analysis: A simulation study.Communications in Statistics: Simulation and Computation 3, 271–298.Google Scholar
- Spiegelhalter, D.et al. (1996) Bayesian Inference Using Gibbs Sampling, Version 0.5, (version ii).MRC Biostatistics Unit, Cambridge.Google Scholar