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Bayesian Analysis and Model Selection for Interval‐Censored Survival Data
Author(s) -
Sinha Debajyoti,
Chen MingHui,
Ghosh Sujit K.
Publication year - 1999
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.0006-341x.1999.00585.x
Subject(s) - censoring (clinical trials) , covariate , statistics , proportional hazards model , survival analysis , bayesian probability , model selection , confidence interval , accelerated failure time model , computer science , data set , econometrics , mathematics
Summary. Interval‐censored data occur in survival analysis when the survival time of each patient is only known to be within an interval and these censoring intervals differ from patient to patient. For such data, we present some Bayesian discretized semiparametric models, incorporating proportional and nonproportional hazards structures, along with associated statistical analyses and tools for model selection using sampling‐based methods. The scope of these methodologies is illustrated through a reanalysis of a breast cancer data set (Finkelstein, 1986, Biometrics 42 , 845–854) to test whether the effect of covariate on survival changes over time.