• Communications for Statistical Applications and Methods
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• v.17 no.5
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• pp.689-696
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• 2010

Interval-censored data are commonly found in studies of diseases that progress without symptoms, which require clinical evaluation for detection. Several techniques have been suggested with independent assumption. However, the assumption will not be valid if observations come from clusters. Furthermore, when the cluster size relates to response variables, commonly used methods can bring biased results. For example, in a study on lymphatic filariasis, a parasitic disease where worms make several nests in the infected person's lymphatic vessels and reside until adulthood, the response variable of interest is the nest-extinction times. Since the extinction times of nests are checked by repeated ultrasound examinations, exact extinction times are not observed. Instead, data are composed of two examination points: the last examination time with living worms and the first examination time with dead worms. Furthermore, as Williamson et al. (2008) pointed out, larger nests show a tendency for low clearance rates. This association has been denoted as an informative cluster size. To analyze the relationship between the numbers of nests and interval-censored nest-extinction times, this study proposes a joint model for the relationship between cluster size and clustered interval-censored failure data.

• The Korean Journal of Applied Statistics
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• v.12 no.2
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• pp.325-333
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• 1999

경북대학교병원에서 1985년에서 1994년까지 위암 때문에 위 절제수술을 받은 1,192명의 환자에 대한 자료를 이용하여 5년 생존율에 관해 분석하고자 한다. 일반적으로 위암 진단을 받은 환자가 수술을 받으려고 할 때 또는 수술을 직후에, 환자의 임상적 특성들을 이용하여 수술후 생존시간과 수술후 5년 생존 여부는 큰 의미가 있다. 그러나 많은 경우에 있어서 실제 임상자료는 연구가 진행 중에 있으므로 생존시간이 우측 중도절단된 형태로 관측되어 기존의 판별분석과 로짓분석을 적용할 수 없다. 본 논문에서는 Buckley와 James가 제안한 의사확률변수를 이용하여 수술전과 수술직후, 두 시점에서 중도절단된 자료를 보완하고, 판별분석과 로짓분석을 통하여 수술전과 수술직후에 환자들의 각 특성이 5년 생존여부에 미치는 영향을 분석을 한다.

• The Korean Journal of Applied Statistics
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• v.20 no.2
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• pp.313-322
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• 2007

Several robust censored depth regression methods are compared under contamination. Park and Hwang(2003) suggested a way to circumvent the censoring issue by incorporating Kaplan-Meier type weight in halfspace regression depth and Park(2003) used a similar technique to simplicial regression depth. Hubert et al. (2001) suggested a high breakdown point regression depth based on projection called rcent. A new method to implement censoring in rcent is suggested and compared with two precedents under various contamination and censoring schemes.

• Journal of the Korean Data and Information Science Society
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• v.9 no.1
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• pp.37-45
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• 1998

We consider the problem of obtaining the confidence bands for the survival function with incomplete data. It is a rather simple procedure for constructing confidence bands of survival function. This method uses the weak convergence of normalized cumulative hazard estimator to a mean zero Gaussian process whose distribution can be easily approximated through simulation. Finally, we compare the performance of the proposed confidence bands through Monte Carlo simulation and we applied to construct the proposed bands with the Leukemia patient data.

• The Korean Journal of Applied Statistics
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• v.22 no.3
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• pp.595-606
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• 2009

Two tests were introduced for comparing several survival functions with doubly interval-censored data and illustrated with data surveyed by Korean Cancer Prevention Study (Jee et al., 2005). The test which extended Kim et al. (2006)'s test to the doubly interval-censored data has an advantage over Sun (2006)'s test in terms of saving computation time because the proposed test only depends on the size of risk set, and also the proposed test is applicable to continuous failure time data as well as discrete failure time data unlike Sun's test. Comparing male with female groups on the incubation time of diabetes was highly different and the survival of female group was longer than that of male one. Regardless of gender, the difference in survival functions of four age groups was highly significant with p-value of less than 0.001. This trend was more remarkable for female group than for male one. Simulation results showed that the significance level of both tests was well controlled and the proposed test was better than Sun's test in terms of power.

• The Korean Journal of Applied Statistics
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• v.29 no.7
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• pp.1311-1327
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• 2016

We propose a multi-state model for analyzing semi-competing risks data with interval-censored or missing intermediate events. This model is an extension of the 'illness-death model', which composes three states, such as 'healthy', 'diseased', and 'dead'. The state of 'diseased' can be considered as an intermediate event. Two more states are added into the illness-death model to describe missing events caused by a loss of follow-up before the end of the study. One of them is a state of 'LTF', representing a lost-to-follow-up, and the other is an unobservable state that represents the intermediate event experienced after LTF occurred. Given covariates, we employ the Cox proportional hazards model with a normal frailty and construct a full likelihood to estimate transition intensities between states in the multi-state model. Marginalization of the full likelihood is completed using the adaptive Gaussian quadrature, and the optimal solution of the regression parameters is achieved through the iterative Newton-Raphson algorithm. Simulation studies are carried out to investigate the finite-sample performance of the proposed estimation procedure in terms of the empirical coverage probability of the true regression parameter. Our proposed method is also illustrated with the dataset adapted from Helmer et al. (2001).

• The Korean Journal of Applied Statistics
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• v.11 no.1
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• pp.113-127
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• 1998

There have been a great deal of interests in comparing two survival distributins in clinical trials. This paper compares some well-known statistical methods for testing the equality of two survival distributions. Simulation studies also provide some insights into the properties of these test statistics across several types of survival distributions and degrees of censorship.

• The Korean Journal of Applied Statistics
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• v.25 no.2
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• pp.311-319
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• 2012

The simplest and the most important distribution in survival analysis is exponential distribution. Koziol and Green (1976) derived Cram$\acute{e}$r-von Mises statistic's randomly censored version based on the Kaplan-Meier product limit estimate of the distribution function; however, it could not be practical for a real data set since the statistic is for testing a simple goodness of fit hypothesis. We generalized it to the composite hypothesis for exponentiality with an unknown scale parameter. We also considered the classical Kolmogorov-Smirnov statistic and generalized it by the exact same way. The two statistics are compared through a simulation study. As a result, we can see that the generalized Koziol-Green statistic has better power in most of the alternative distributions considered.

• The Korean Journal of Applied Statistics
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• v.27 no.1
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• pp.1-11
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• 2014

The Weighted Logrank test and its special case, Logrank test are widely used to compare survival distributions; however, these methods are inappropriate when the sample size is small or censoring distributions are not equal since they use test statistics from approximate distributions. A permutation test can be an alternative for small sample cases; however, this should be used only when censoring distributions are equal. To handle cases with small sample size and unequal censoring distributions, the permutation-imputation method was developed to compare two survival distributions. In this paper, approximate method, permutation method and permutation-imputation method were compared using a Logrank test and Prentice-Wilcoxon test for three or more survival distributions comparison.

• Korea journal of population studies
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• v.26 no.2
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• pp.91-110
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• 2003

This study analyzed data from 1997 Korea's Behavioral Risk Factor Surveillance System Survey collected through telephone questionings based on the multi-stage stratified random sampling. We categorized respondents into those who had ever drunk an alcoholic beverage in the last month and those who didn't and, referring to the World Health Organization's guideline, the former group were further categorized into low risk drinking group and high risk drinking group. Employing bivariate probit regression analyses with censoring on independent variables such as preferred type of alcoholic beverage, the number of types of beverages consumed, age, marital status, education, occupation, residential area, current smoking, body mass index and stress suggested (1) that those who prefer soju are more likely to involve high risk drinking than those who and prefer the other alcoholic beverages (2) that those who are relatively older, who live without a partner, who have jobs, who. are vulnerable to stress, or who enjoy more than one type of beverage are more likely to be exposed to high risk drinking than the others.