• 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.

• Communications for Statistical Applications and Methods
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• 제30권1호
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• pp.1-19
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• 2023

Observational data with missing or incomplete data are common in biomedical research. Multiple imputation is an effective approach to handle missing data with the ability to decrease bias while increasing statistical power and efficiency. In recent years propensity score (PS) matching has been increasingly used in observational studies to estimate treatment effect as it can reduce confounding due to measured baseline covariates. In this paper, we describe in detail approaches to competing risk analysis in the setting of incomplete observational data when using PS matching. First, we used multiple imputation to impute several missing variables simultaneously, then conducted propensity-score matching to match statin-exposed patients with those unexposed. Afterwards, we assessed the effect of statin exposure on the risk of heart failure-related hospitalizations or emergency visits by estimating both relative and absolute effects. Collectively, we provided a general methodological framework to assess treatment effect in incomplete observational data. In addition, we presented a practical approach to produce overall cumulative incidence function (CIF) based on estimates from multiple imputed and PS-matched samples.

• Journal of the Korean Data and Information Science Society
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• 제28권4호
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• pp.947-955
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• 2017

A competing risk analysis has been applied when subjects experience more than one type of end points. Geskus (2011) showed three types of estimators of CIF are equivalent under left truncated and right censored data. We extend his approach to an interval censored competing risk data by using a modified risk set and evaluate their performance under several sample sizes. These estimators show very similar results. We also suggest a test statistic combining Sun's test for interval censored data and Gray's test for right censored data. The test sizes and powers are compared under several cases. As a real data application, the suggested method is applied a data where the feasibility of the vaccine to HIV was assessed in the injecting drug uses.

• Communications for Statistical Applications and Methods
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• 제23권6호
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• pp.555-562
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• 2016

Interval censored data often occur in an observational study where the subject is followed periodically. Instead of observing an exact failure time, two inspection times that include it are available. There are several methods to analyze interval censored failure time data (Sun, 2006). However, in the presence of competing risks, few methods have been suggested to estimate covariate effect on interval censored competing risk data. A sub-distribution hazard model is a commonly used regression model because it has one-to-one correspondence with a cumulative incidence function. Alternatively, Klein and Andersen (2005) proposed a pseudo-value approach that directly uses the cumulative incidence function. In this paper, we consider an extension of the pseudo-value approach into the interval censored data to estimate regression coefficients. The pseudo-values generated from the estimated cumulative incidence function then become response variables in a generalized estimating equation. Simulation studies show that the suggested method performs well in several situations and an HIV-AIDS cohort study is analyzed as a real data example.

• Journal of the Korean Data and Information Science Society
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• 제26권6호
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• pp.1271-1281
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• 2015

본 논문에서는 '제1회 산재보험패널조사'에서 제공된 자료를 이용하여 산재 근로자의 경제 활동 유형의 특성을 연구하였다. 조사 대상자는 2012년도에 산재 요양을 종결한 근로자이며 총 2,000명이 지역, 장해등급 및 재활서비스 이용여부로 층화계통추출되었다. 본 연구에서는 근로자가 산재 후 참여하는 경제활동의 유형으로 원직장복귀뿐만 아니라 다른 직장으로의 재취업의 가능성을 고려하여 이러한 경제활동으로의 이동에 어떤 요인이 영향을 미치는지 조사하고자 한다. 원직장복귀에 영향을 미치는 요인을 분석하기 위하여 총 1,463명의 연구 대상자에게 경쟁위험 분석방법을 적용하였다. 또한 경제활동상태에 영향을 미치는 요인을 세 가지 유형 (산재 근로자의 특성, 재해 사업장의 특성, 산업재해의 특성)으로 나누어 통합 분석을 시행하였다. 분석 결과를 통해 학력이 높고 근로기간이 길수록 원직장복귀가 빨라짐을 알 수 있었다. 또한 연령이 높고, 기능원 및 관련 기능직에 종사자이며, 장해의 정도가 심한 산재 근로자가 원직장복귀까지 더 오랜 시간이 걸렸음을 알 수 있었다.

• Journal of the Korean Data and Information Science Society
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• 제26권6호
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• pp.1573-1582
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• 2015

In lifetime data analysis, it is generally known that the lifetimes of test items may not be recorded exactly. There are also situations wherein the withdrawal of items prior to failure is prearranged in order to decrease the time or cost associated with experience. Moreover, it is generally known that more than one cause or risk factor may be present at the same time. Therefore, analysis of censored competing risks data are needed. In this article, we derive the Bayes estimators for the entropy function under the exponential distribution with an unknown scale parameter based on multiply Type II censored competing risks data. The Bayes estimators of entropy function for the exponential distribution with multiply Type II censored competing risks data under the squared error loss function (SELF), precautionary loss function (PLF) and DeGroot loss function (DLF) are provided. Lindley's approximate method is used to compute these estimators.We compare the proposed Bayes estimators in the sense of the mean squared error (MSE) for various multiply Type II censored competing risks data. Finally, a real data set has been analyzed for illustrative purposes.

• Asian Pacific Journal of Cancer Prevention
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• 제15권15호
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• pp.6253-6255
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• 2014

Background: Colorectal cancer (CRC) is considered to be a main cause of malignancy-related death in the world, being commonly diagnosed in both men and women. It is the third leading cause of cancer dependent death in the world and there are one million new cases diagnosed per year. In Iran the incidence of colorectal cancer has increased during the last 25 years and it is the fifth cause of cancer in men and the third in women. Materials and Methods: In this article we analyzed the survival of 475 colorectal patients of Taleghani hospital in Tehran with the semi-parametric competing-risks model. Results: There were 55% male cases and at the time of the diagnosis most of the patients were between 48 and 67years old. The probability of a patient death from colorectal cancer with survival of more than 25 years was about 0.4. Body mass index, height, tumour site and gender had no influence. Conclusions: According to these data and by using semi-parametric competing-risks method, we found out that only age at diagnosis has a significant effect on these patient survival time.

• 노동경제논집
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• 제43권3호
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• pp.1-31
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• 2020

본 연구는 중고령층 자영업 이탈에 영향을 주는 요인을 자영업 이탈 이유를 고려하여 분석하였다. 분석에는 고령화 연구 패널(KLoSA)의 1~6차 및 직업력 자료를 이용하였다. 자영업 이탈 이유 중 경영상 어려움으로 인해 이탈한 집단이 다른 이유로 이탈한 집단보다 경제적 어려움에 처할 가능성이 큼에 따라 이들을 중심으로 분석하였다. 분석 모형으로는 경영상 어려움으로 인한 이탈만을 실패로 정의하는 경쟁위험회귀모형(competing risk regression models)을 이용하였다. 그 결과, 자영업 이탈 요인으로 알려졌던 성별, 연령, 학력 변수의 유의성이 사라졌다. 반면 동종 산업 경력이 자영업 이탈 위험률을 낮추는 경향이 있음을 확인하였다. 이에 따라 경제적 어려움과 실업에 직면할 위험이 큰 자영업 이탈을 방지하기 위해서는 동종 산업 내 경력이 부족한 채로 자영업을 시작하는 중고령층을 지원하는 방안이 필요함을 제시할 수 있다.

• Journal of Preventive Medicine and Public Health
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• 제54권1호
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• pp.55-62
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• 2021

Objectives: After heart disease, brain stroke (BS) is the second most common cause of death worldwide, underscoring the importance of understanding preventable and treatable risk factors for the outcomes of BS. This study aimed to model the survival of patients with BS in the presence of competing risks. Methods: This longitudinal study was conducted on 332 patients with a definitive diagnosis of BS. Demographic characteristics and risk factors were collected by a validated checklist. Patients' mortality status was investigated by telephone follow-up to identify deaths that may be have been caused by stroke or other factors (heart disease, diabetes, high cholesterol, etc.). Data were analyzed by the Lunn-McNeil approach at alpha=0.1. Results: Older age at diagnosis (59-68 years: adjusted hazard ratio [aHR], 2.19; 90% confidence interval [CI], 1.38 to 3.48; 69-75 years: aHR, 5.04; 90% CI, 3.25 to 7.80; ≥76 years: aHR, 5.30; 90% CI, 3.40 to 8.44), having heart disease (aHR, 1.65; 90% CI, 1.23 to 2.23), oral contraceptive pill use (women only) (aHR, 0.44; 90% CI, 0.24 to 0.78) and ischemic stroke (aHR, 0.52; 90% CI, 0.36 to 0.74) were directly related to death from BS. Older age at diagnosis (59-68 years: aHR, 21.42; 90% CI, 3.52 to 130.39; 75-69 years: aHR, 16.48; 90% CI, 2.75 to 98.69; ≥76 years: aHR, 26.03; 90% CI, 4.06 to 166.93) and rural residence (aHR, 2.30; 90% CI, 1.15 to 4.60) were directly related to death from other causes. Significant risk factors were found for both causes of death. Conclusions: BS-specific and non-BS-specific mortality had different risk factors. These findings could be utilized to prescribe optimal and specific treatment.

• Annals of dermatology
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• 제30권6호
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• pp.668-675
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• 2018

Background: Drug survival, defined as the time until discontinuation, is a parameter reflecting real-world therapeutic effectiveness. Few studies have examined the influence of economic factors on the drug survival of biologic agents for psoriasis, particularly in Asian countries. Objective: To determine the drug survival for ustekinumab in real-life settings and investigate the factors affecting drug survival for psoriasis patients in Korea. Methods: We evaluated 98 psoriasis patients who were treated with ustekinumab at a single center. We analyzed the efficacy and drug survival of ustekinumab. Cox proportional hazard analysis and competing risk regression analysis were performed to reveal the factors affecting the drug survival of ustekinumab. Results: The overall mean drug survival was 1,596 days (95% confidence interval [CI], 904~2,288). Among the 39 cessations of ustekinumab treatment, 9 (23.1%) patients discontinued treatment after experiencing satisfactory results. Multivariate Cox proportional hazard analysis revealed that paying on patients' own expense was the major predictor for the discontinuation of ustekinumab (hazard ratio [HR], 9.696; 95% CI, 4.088~22.998). Competing risk regression analysis modeling of discontinuation because of factors other than satisfaction of an event also revealed that ustekinumab treatment at the patient's expense (HR, 4.138; 95% CI, 1.684~10.168) was a predictor of discontinuation rather than satisfaction. Conclusion: The results of our study revealed that the cost of biologics treatment affects the drug survival of ustekinumab and suggested that economic factors affect the drug survival of ustekinumab treatment in Korea.