• Communications for Statistical Applications and Methods
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• 제28권2호
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• pp.151-159
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• 2021

In this article, our interest is to estimate the association between consecutive gap times which are subject to interval censoring. Such data are referred as doubly interval censored data (Sun, 2006). In a context of serial event, an induced dependent censoring frequently occurs, resulting in biased estimates. In this study, our goal is to propose a Kendall's 𝜏 based association measure for doubly interval censored data. For adjusting the impact of induced dependent censoring, the inverse probability censoring weighting (IPCW) technique is implemented. Furthermore, a multiple imputation technique is applied to recover unknown failure times owing to interval censoring. Simulation studies demonstrate that the suggested association estimator performs well with moderate sample sizes. The proposed method is applied to a dataset of children's dental records.

• 한국통계학회:학술대회논문집
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• 한국통계학회 2004년도 학술발표논문집
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• pp.237-241
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• 2004

In standard time-to-event or survival analysis, the occurrence times of the event of interest are observed exactly or are right-censored. However in certain situations such as the AIDS data, the incubation period which is the time between HIV infection time and the diagnosis of AIDS is usually doubly censored. That is the HIV infection time Is interval censored and also the time of the diagnosis of AIDS is right censored. In this paper, we Impute the Interval censored infection time using the conditional mean imputation and estimate the coefficient factor of the regression analysis for the incubation period using Gibbs sampler. We applied parametric and semi-parametric methods for the analysis of the Incubation period and compared the results.

• 응용통계연구
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• 제22권3호
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• pp.607-616
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• 2009

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.

• 한국통계학회:학술대회논문집
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• 한국통계학회 2004년도 학술발표논문집
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• pp.243-248
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• 2004

The timings of two successive events of interest may not be measurable, instead it may be right censored or interval censored; this data structure is called doubly censored data. In the study of HIV, two such events are the infection with HIV and the onset of AIDS. These data have been analyzed by authors under the assumption that infection time and induction time are independent. This paper investigates the regression problem when two events arc modeled to allow the presence of a possible relation between two events as well as a subject-specific effect. We derive the estimation procedure based on Goetghebeur and Ryan's (2000) piecewise exponential model and Gauss-Hermite integration is applied in the EM algorithm. Simulation studies are performed to investigate the small-sample properties and the method is applied to a set of doubly censored data from an AIDS cohort study.

• Communications for Statistical Applications and Methods
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• 제23권4호
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• pp.343-353
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• 2016

In doubly-censored data, an originating event time and a terminating event time are interval-censored. In certain analyses of such data, a researcher might be interested in the elapsed time between the originating and terminating events as well as regression modeling with risk factors. Therefore, in this study, we introduce a model evaluation method to measure the predictive ability of a model based on negative predictive values. We use a semiparametric estimate of the predictive accuracy to provide a simple and flexible method for model evaluation of doubly-censored survival outcomes. Additionally, we used simulation studies and tested data from a prostate cancer trial to illustrate the practical advantages of our approach. We believe that this method could be widely used to build prediction models or nomograms.

• 응용통계연구
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• 제22권3호
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• pp.595-606
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• 2009

이중구간중도절단된 자료의 생존함수를 비교하기 위한 두 검정법을 소개하고 한국인 암 예방연구 (Jee 등, 2005) 자료에 적용하여 당뇨병 잠복시간의 분포를 성별과 연령에 따라 비교하였다. Kim 등 (2006)의 검정법을 이중구간중도절단된 자료로 확장한 검정법은 위험집합의 크기에만 의존하는 가중값을 사용하기 때문에 대용량 자료의 분석에서 Sun (2006)의 검정법보다 계산 시간을 대폭 줄일 수 있으며, 이산형 생존자료뿐만 아니라 연속형 생존자료에도 적용가능한 장점이 있다. 당뇨병의 잠복시간이 성별에 따라 매우 유의하게 달랐으며 여자의 잠복시간이 남자보다 긴 것으로 나타났다. 4개 연령그룹 간 당뇨병의 잠복시간도 성별에 관계 없이 매우 유의하게 달랐으며 여자의 경우가 남자의 경우보다 그 차이가 훨씬 더 유의했다. 한편, 소표본 모의실험을 통해 제안한 검정법과 Sun (2006)의 검정법의 검정력을 비교하였으며 제안한 검정법의 검정력이 Sun (2006)의 검정법보다 더 좋은 것으로 나타났다.