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Comparing Imputation Methods for Doubly Censored DataComparing Imputation Methods for Doubly Censored Data

Other Titles
Comparing Imputation Methods for Doubly Censored Data
Authors
유한나이재원
Issue Date
2009
Publisher
한국통계학회
Keywords
Doubly censored data; conditional mean imputation; approximate Bayesian bootstrap; Gibbs sampler.
Citation
응용통계연구, v.22, no.3, pp.607 - 616
Indexed
KCI
Journal Title
응용통계연구
Volume
22
Number
3
Start Page
607
End Page
616
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/121493
ISSN
1225-066X
Abstract
In many epidemiological studies, the occurrence times of the event of interest are right-censored or interval censored. In certain situations such as the AIDS data, however, the incubation period which is the time between HIV infection and the diagnosis of AIDS is usually doubly censored. In this paper, we impute the interval censored HIV infection time using three imputation methods. Mid imputation, conditional mean imputation and approximate Bayesian bootstrap are implemented to obtain right censored data, and then Gibbs sampler is used to estimate the coefficient factor of the incubation period. By using Bayesian approach, flexible modeling and the use of prior information is available. We applied both parametric and semi-parametric methods for estimating the effect of the covariate and compared the imputation results incorporating prior information for the covariate effects.
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