• 한국인구학
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• 제26권2호
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• pp.91-110
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• 2003

고위험 음주는 개인 건강 뿐 아니라 사회에 막대한 부담을 초래한다. 본 연구는 보건복지부와 서울대학교외 건강위험요인 전국조사 자료에 중도 절단 이변량 프로빗 모형(bivariate probit model with censoring)을 활용하여 한국 사회에서 15세 이상 인구계층의 고위험 음주에 미치는 요인을 규명하였다. 연구결과에 따르면 우리나라에서 소주가 음주 주종 중 고위험 음주와 가장 관련성이 큰 주종임이 밝혀졌다. 그동안 소주의 문제점에 관해서는 대부분 인식하고 있었으나 전국 조사 자료 및 최신 통계분석방법을 적용한 연구가 전무하여 실증적인 뒷받침이 되지 않았던 사실이 밝혀진 셈이다. 또한 나이가 많은 계층, 배우자와 동거하지 않는 계층, 경제활동에 종사하는 계층, 스트레스에 취약한 계층, 다양한 주종을 소비하는 계층이 각각 상대적으로 그렇지 않는 계층에 비해 고위험 음주를 할 가능성이 높았다. 본 연구결과로써 한국 사회에서 음주로 인한 위해를 감소시키기 위해서는 주종별로 차별화된 정책과 함께 고위험 음주 가능성이 상대적으로 높은 인구계층을 대상으로 정부 및 민간 노력이 집중되어야 한다는 정책적 시사점을 도출할 수 있었다.

• Journal of the Korean Data and Information Science Society
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• 제7권1호
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• pp.21-35
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• 1996

In this paper, estimation and prediction procedures are discussed for grneral situation in which the failure time follows the independent density $f_{i}({\varepsilon}_{i})$ for the accelerated life testing under Type II censoring. In the context of accelerated life test experiment, procedures are given for estimating the parameters in the Eyring model, and for estimating mean life at a given future stress level. The procedures given are conditional confidence interval procedures, obtained by conditioning on ancillary statistics. A comparison is made of these procedures and procedures based on asymptotic properties of the maximum, likelihood estimates.

• Communications for Statistical Applications and Methods
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• 제12권3호
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• pp.783-795
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• 2005

We consider to obtain an estimate of bivariate survival function for the right censored data with the assumption that the two components of censoring vector are independent. The estimate is derived from an ad hoc approach based on the representation of survival function. Then the resulting estimate can be considered as an extension of the Susarla- Van Ryzin estimate to the bivariate data. Also we show the consistency and weak convergence for the proposed estimate. Finally we compare our estimate with Dabrowska's estimate with an example and discuss some properties of our estimate with brief comment on the extension to the multivariate case.

• 한국신뢰성학회지:신뢰성응용연구
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• 제16권1호
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• pp.56-63
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• 2016

Purpose: The purpose of this study is to point out that the Kaplan-Meier method is not valid to calculate the survival probability or failure probability (risk) in the presence of competing risks and to introduce more valid method of cumulative incidence function. Methods: Survival analysis methods have been widely used in biostatistics division. However the same methods have not been utilized in reliability division. Especially competing risks cases, where several causes of failure occur and the occurrence of one event precludes the occurrence of the other events, are scattered in reliability field. But they are not noticed in the realm of reliability expertism or they are analysed in the wrong way. Specifically Kaplan-Meier method which assumes that the censoring times and failure times are independent is used to calculate the probability of failure in the presence of competing risks, thereby overestimating the real probability of failure. Hence, cumulative incidence function is introduced and sample competing risks data are analysed using cumulative incidence function and some graphs. Finally comparison of cumulative incidence functions and regression type analysis are mentioned briefly. Results: Cumulative incidence function is used to calculate the survival probability or failure probability (risk) in the presence of competing risks and some useful graphs depicting the failure trend over the lifetime are introduced. Conclusion: This paper shows that Kaplan-Meier method is not appropriate for the evaluation of survival or failure over the course of lifetime. In stead, cumulative incidence function is shown to be useful. Some graphs using the cumulative incidence functions are also shown to be informative.

• Asian Pacific Journal of Cancer Prevention
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• 제14권2호
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• pp.1107-1110
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• 2013

Intrahepatic cholangiocarcinoma (ICC), one of the primary liver cancers, is frequent in the northeastern part of Thailand. Surgical resection remains the best method of treatment, but patients suffering from ICC usually present at a late stage of the disease. Studies of survival and prognostic factors after surgery remain rare. The aim here was to evaluate the survival rate and factors affecting the survival of patients with intrahepatic cholangiocarcinoma after surgery. The study used a retrospective cohort design. The subjects were 73 consecutive patients with ICC, who were admitted for surgery to Srinagarind Hospital, Khon Kaen University, during the period 2005-2009. The censoring date was 31 December, 2011, data being evaluated using uni- and multivariate analyses. Postoperative survival analysis was performed by the Kaplan-Meier method, and the Cox proportional hazard model was used to identify independent prognostic factors. The total follow-up time was 99 person-years. The total number of deaths was 59, giving a mortality rate of 59 per 100 person-years. The cumulative 1-, 3-, and 5-year survival rates were 52.1%, 21.7%, and 11.2%, respectively. The median duration of survival after resection was 12.4 months. Univariate analysis revealed stage of disease, lymph node metastasis, histological type, histological grade and macroscopic classification to be statistically significant (p-value<0.05) prognostic factors. In the multivariate analysis, only macroscopic classification was statistically significant (p-value<0.05). In conclusion, macroscopic classification was the only independent factor found to be significantly associated with survival following surgical treatment of ICC.