• Title/Summary/Keyword: 중도절단표본

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Analysis of Tumorigenicity Data with Informative Censoring (종속적인 중도절단을 가진 동물종양 자료의 분석을 위한 모형)

  • Kim, Jin-Heum;Kim, Youn-Nam
    • The Korean Journal of Applied Statistics
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    • v.23 no.5
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    • pp.871-882
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    • 2010
  • In animal tumorigenicity data, the occurrence time of tumor is not observed because the existence of a tumor is examined only at either time of natural death or time of sacrifice for the animal. A three-state model (Health-Tumor onset-Death) is widely used to model the incomplete data. In this paper, we employed a frailty effect into the three-state model to incorporate the dependency of death on tumor occurrence when the time of natural death works as an informative censoring against the tumor onset time. For the inference of parameters, then the EM algorithm is considered in order to deal with missing quantities of tumor onset time and random frailty. The proposed method is applied to the bladder tumor data taken from Lindsey and Ryan (1993, 1994) and a simulation study is performed to show the behavior of the proposed estimators.

Joint model of longitudinal data with informative observation time and competing risk (결시적 자료에서 관측 중단을 모형화하기 위해 사용되는 경쟁 위험의 적용과 결합 모형)

  • Kim, Yang-Jin
    • The Korean Journal of Applied Statistics
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    • v.29 no.1
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    • pp.113-122
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    • 2016
  • Longitudinal data often occur in prospective follow-up studies. Joint model for longitudinal data and failure time has been applied on several works. In this paper, we extend it to the case where longitudinal data involve informative observation time process as well as competing risks survival times. We use a likelihood approach and derive an EM algorithm to obtain maximum likelihood estimate of parameters. A suggested joint model allows us to make inferences for three components: longitudinal outcome, observation time process and competing risk failure time. In addition, we can test the association among these components. In this paper, liver cirrhosis patients' data is analyzed. The relationship between prothrombin times measured at irregular visiting times and drop outs is investigated with a joint model.

Statistical analysis of estimating incubation period distribution and case fatality rate of COVID-19 (COVID-19 바이러스 잠복 시간 분포 추정과 치사율 추정을 위한 생존 분석의 적용)

  • Ki, Han Jeong;Kim, Jieun;Kim, Sohee;Park, Juwon;Lee, Joohaeng;Kim, Yang-Jin
    • The Korean Journal of Applied Statistics
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    • v.33 no.6
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    • pp.777-789
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    • 2020
  • COVID-19 has been rapidly spread world wide since late December 2019. In this paper, our interest is to estimate distribution of incubation time defined as period between infection of virus and the onset. Due to the limit of accessibility and asymptomatic feature of COVID-19 virus, the exact infection and onset time are not always observable. For estimation of incubation time, interval censoring technique is implemented. Furthermore, a competing risk model is applied to estimate the case fatality and cure fraction. Based on the result, the mean incubation time is about 5.4 days and the fatality rate is higher for older and male patient and the cure rate is higher at younger,female and asymptomatic patient.