• Journal of the Korean Statistical Society
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• 제20권1호
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• pp.85-92
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• 1991

A set of conditions ensuring local asymptotic normality for independent but not necessarily identically distributed observations in semiparametric models is presented here. The conditions are turned out to be more direct and easier to verify than those of Oosterhoff and van Zwet(1979) in semiparametric models. Examples considered include the simple linear regression model and Cox's proportional hazards model without censoring where the covariates are not random.

• The Korean Journal of Pain
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• 제33권2호
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• pp.153-165
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• 2020

Background: Well-validated risk prediction models help to identify individuals at high risk of diseases and suggest preventive measures. A recent systematic review reported lack of validated prediction models for low back pain (LBP). We aimed to develop prediction models to estimate the 8-year risk of developing LBP and its recurrence. Methods: A population based prospective cohort study using data from 435,968 participants in the National Health Insurance Service-National Sample Cohort enrolled from 2002 to 2010. We used Cox proportional hazards models. Results: During median follow-up period of 8.4 years, there were 143,396 (32.9%) first onset LBP cases. The prediction model of first onset consisted of age, sex, income grade, alcohol consumption, physical exercise, body mass index (BMI), total cholesterol, blood pressure, and medical history of diseases. The model of 5-year recurrence risk was comprised of age, sex, income grade, BMI, length of prescription, and medical history of diseases. The Harrell's C-statistic was 0.812 (95% confidence interval [CI], 0.804-0.820) and 0.916 (95% CI, 0.907-0.924) in validation cohorts of LBP onset and recurrence models, respectively. Age, disc degeneration, and sex conferred the highest risk points for onset, whereas age, spondylolisthesis, and disc degeneration conferred the highest risk for recurrence. Conclusions: LBP risk prediction models and simplified risk scores have been developed and validated using data from general medical practice. This study also offers an opportunity for external validation and updating of the models by incorporating other risk predictors in other settings, especially in this era of precision medicine.

• Journal of Gastric Cancer
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• 제15권4호
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• pp.238-245
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• 2015

Purpose: This study aimed to identify time-dependent prognostic factors and demonstrate the time-dependent effects of important prognostic factors in patients with advanced gastric cancer (AGC). Materials and Methods: We retrospectively evaluated 3,653 patients with AGC who underwent curative standard gastrectomy between 1991 and 2005 at the Korea Cancer Center Hospital. Multivariate survival analysis with Cox proportional hazards regression was used in the analysis. A non-proportionality test based on the Schoenfeld residuals (also known as partial residuals) was performed, and scaled Schoenfeld residuals were plotted over time for each covariate. Results: The multivariate analysis revealed that sex, depth of invasion, metastatic lymph node (LN) ratio, tumor size, and chemotherapy were time-dependent covariates violating the proportional hazards assumption. The prognostic effects (i.e., log of hazard ratio [LHR]) of the time-dependent covariates changed over time during follow-up, and the effects generally diminished with low slope (e.g., depth of invasion and tumor size), with gentle slope (e.g., metastatic LN ratio), or with steep slope (e.g., chemotherapy). Meanwhile, the LHR functions of some covariates (e.g., sex) crossed the zero reference line from positive (i.e., bad prognosis) to negative (i.e., good prognosis). Conclusions: The time-dependent effects of the prognostic factors of AGC are clearly demonstrated in this study. We can suggest that time-dependent effects are not an uncommon phenomenon among prognostic factors of AGC.

• Journal of the Korean Statistical Society
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• 제34권3호
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• pp.235-244
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• 2005

In recent years, theoretical properties of Bayesian nonparametric survival models have been studied and the conclusion is that although there are pathological cases the popular prior processes have the desired asymptotic properties, namely, the posterior consistency and the Bernstein-von Mises theorem. In this study, through a simulation experiment, we study the finite sample properties of the Bayes estimator and compare it with the frequentist estimators. To our surprise, we conclude that in most situations except that the prior is highly concentrated at the true parameter value, the Bayes estimator performs worse than the frequentist estimators.

• Communications for Statistical Applications and Methods
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• 제22권4호
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• pp.313-320
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• 2015

In this research, we compare the efficiency of two test procedures proposed by Prentice and Gloeckler (1978) and Park and Hong (2009) for grouped data with possible right censored observations. Both test statistics were derived using the likelihood ratio principle, but under different semi-parametric models. We review the two statistics with asymptotic normality and consider obtaining empirical powers through a simulation study. The simulation study considers two types of models the location translation model and the scale model. We discuss some interesting features related to the grouped data and obtain null distribution functions with a re-sampling method. Finally we indicate topics for future research.

• 한국수자원학회논문집
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• 제42권6호
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• pp.457-464
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• 2009

본 논문에서는 상수관로의 파손자료를 이용하여 관로의 위험률을 산정하기 위해 사용되는 비례위험모형의 관로의 순차적 파손시간 예측정확도를 분석하고 이를 이용하여 관로의 경제적 교체 시간구간을 산정할 수 있는 방법론을 제시하였다. 비례위험모형에 기초한 생존함수를 이용하여 연구대상 관로들의 순차적 파손시간을 예측하고 이들을 기록된 파손시간과의 차이를 분석하였다. 이를 통하여 비례위험모형의 파손시간 예측 오차를 최소화하는 생존확률은 0.70인 것으로 결정되었으며, 세 번째 파손으로부터 일곱 번째 파손에 대한 모형만이 관로의 파손시간을 예측하는데 적합한 것으로 분석되었다. 생존확률 0.70과 순차적 파손사건에 대한 생존함수의 하한 및 상한을 이용하여 예제로 사용된 관로에 대해 예측된 파손시간의 95% 신뢰구간의 하한 및 상한을 추정하였다. 예측된 파손시간의 95% 신뢰 구간의 하한과 상한을 이용하여 관로 파손 경향모형인 General Pipe Break Prediction Models(GPBM)을 구축하고 이들을 관로의 한계파괴율과 결합하여 시간에 대한 해를 구하므로써 경제적 교체 시간구간을 산정하였다.

• 한국생물정보학회:학술대회논문집
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• 한국생물정보시스템생물학회 2005년도 BIOINFO 2005
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• pp.357-360
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• 2005

In this paper we consider the well-known semiparametric proportional hazards (PH) models for survival analysis. These models are usually used with few covariates and many observations (subjects). But, for a typical setting of gene expression data from DNA microarray, we need to consider the case where the number of covariates p exceeds the number of samples n. For a given vector of response values which are times to event (death or censored times) and p gene expressions (covariates), we address the issue of how to reduce the dimension by selecting the significant genes. This approach enable us to estimate the survival curve when n < < p. In our approach, rather than fixing the number of selected genes, we will assign a prior distribution to this number. The approach creates additional flexibility by allowing the imposition of constraints, such as bounding the dimension via a prior, which in effect works as a penalty. To implement our methodology, we use a Markov Chain Monte Carlo (MCMC) method. We demonstrate the use of the methodology to diffuse large B-cell lymphoma (DLBCL) complementary DNA(cDNA) data.

• Asian Pacific Journal of Cancer Prevention
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• 제16권12호
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• pp.5081-5084
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• 2015

Background: Breast cancer is a malignant tumor that starts from cells of the breast and is seen mainly in women. It's the most common cancer in women worldwide and is a major threat to health. The purpose of this study was to fit a Cox proportional hazards model for prediction and determination of years of survival in Iranian patients. Materials and Methods: A total of 366 patients with breast cancer in the Cancer Research Center were included in the study. A Cox proportional hazard model was used with variables such as tumor grade, number of removed positive lymph nodes, human epidermal growth factor receptor 2 (HER2) expression and several other variables. Kaplan-Meier curves were plotted and multi-years of survival were evaluated. Results: The mean age of patients was 48.1 years. Consumption of fatty foods (p=0.033), recurrence (p<0.001), tumor grade (p=0.046) and age (p=0.017) were significant variables. The overall 1- year, 3-year and 5-year survival rates were found to be 93%, 75% and 52%. Conclusions: Use of covariates and the Cox proportional hazard model are effective in predicting the survival of individuals and this model distinguished 4 effective factors in the survival of patients.

• 응용통계연구
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• 제31권6호
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• pp.761-769
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• 2018

통계학 연구에 모의실험이 중요하게 쓰이며 중도절단자료를 다루는 생존분석에서도 마찬가지다. 생존분석에서 Cox 모형이 널리 쓰이는데, Cox 모형을 따르는 중도절단자료를 생성하는 방법에 대해 살펴보았다. Bender 등 (Statistics in Medicine, 24, 1713-1723, 2005)은 생존시간을 생성하는 모수적 방법을 제시하였으나 생존시간뿐만 아니라 중도절단시간도 생성해야 중도절단자료를 얻게 된다. 중도절단자료를 생성하기 위한 모수적 방법과 함께 비모수적 방법도 제시하였으며 실제 자료에도 적용해 보았다.

• 응용통계연구
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• 제31권4호
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• pp.529-538
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• 2018

결핵은 높은 이환과 사망을 일으키는 질병으로 현대의학의 발달에 따라 발생률과 사망률은 감소하고 있다. 그러나 한국은 아직까지 OECD 국가 중 결핵 발생률과 사망률이 가장 높다. 이에 따라 한국은 결핵의 예방 및 통제를 위해 여러 정책 사업을 실시하고 있다. 본 연구에서는 공공민간협력(public-private mix) 결핵관리사업이 치료결과에 미치는 영향을 분석하고 결핵환자의 치료 성공에 영향을 미치는 요인을 확인하고자 한다. 질병관리본부에서 관리하는 결핵환자 신고 자료를 이용하여 2012-2015년 전국 결핵 신환자 코호트 약 13만명을 대상으로 분석하였다. 누적 발생 함수(cumulative incidence function)를 이용하여 요인별로 누적 치료 성공률을 비교하였으며. 주 관심사건(치료성공) 및 경쟁사건(사망)을 고려한 두 가지 경쟁위험모형(cause-specific Cox's proportional hazards model and subdistribution hazard model)을 사용하여 분석 결과를 비교하였다.