• 응용통계연구
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• 제29권7호
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• pp.1459-1473
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• 2016

시간의 흐름에 따라 관측되는 경시적(longitudinal) 자료의 경우, 경시적 자료와 생존(survival) 자료가 종종 동시에 수집된다. 이 때 경시적 자료에서 발생하는 결측이 생존자료와의 연관성으로 인해 발생한 무시할 수 없는 결측(non-ignorable missing)이라면, 경시적 자료분석 방법만으로는 두 자료 간의 연관성을 고려하지 않아 독립변수에 대한 효과는 편향된 결과를 얻게 된다. 이러한 문제를 해결하기 위해서 결측의 원인이 생존시간과 연관되어 있으므로 생존모형을 고려하여 불편추정량을 얻기 위해 경시적 자료와 생존자료의 결합모형에 대한 연구가 이루어져 왔다. 본 논문은 경시적 자료의 형태가 영이 많이 존재하는 영과잉 가산자료(zero-inflated count data)와 생존자료의 결합모형을 연구하였다. 경시적 영과잉 가산자료와 생존자료는 각각 허들모형(hurdle model)과 비례위험모형(proportional hazards model)의 부 모형을 적용하였고, 두 부 모형들의 변량효과가 다변량 정규분포를 따른다는 가정을 통하여 결합하였다. 모수의 최우추정법으로 EM 알고리즘을 활용하였고, 추정된 표준오차를 계산하기 위해 프로파일 우도(profile likelihood)를 이용하였다. 최종적으로 모의실험을 통해 두 부 모형의 변량효과 간 상관관계가 존재하는 경우 결합모형이 개별적 모형보다 편의와 포함확률(coverage probability)의 측면에서 더 우수함을 보였다.

• Communications for Statistical Applications and Methods
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• 제5권1호
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• pp.239-263
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• 1998

In this paper, we consider a multistate survival model which incorporates covariates and contains two illness states and two death states. The underlying stochastic process is assumed to follow nonhomogeneous Markov process. The estimates of survival, transition and competing risks probabilities are given via the methods of partial likelihood and nonparametric maximum likelihood. Our discussion is based on the statistical theory of counting process. An illustration is given to the data of patients in a heart transplant program. The goodness of fit procedures are also discussed to check the adequacy of the model.

• Asian Pacific Journal of Cancer Prevention
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• 제16권14호
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• pp.5655-5661
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• 2015

Purpose: To assess the role of two adjuvant chemotherapy regimens, anthracycline-based and CMF on disease free survival and overall survival breast cancer patients by meta-analysis approach in Eastern Mediterranean and Asian countries to determine which is more effective and evaluate the appropriateness and efficiency of two different proposed statistical models. Materials and Methods: Survival curves were digitized and the survival proportions and times were extracted and modeled to appropriate covariates by two multivariate mixed effects models. Studies which reported disease free survival and overall survival curves for anthracycline-based or CMF as adjuvant chemotherapy that were published in English in the Eastern Mediterranean region and Asia were included in this systematic review. The two transformations of survival probabilities (Ln (-Ln(S)) and Ln(S/ (1-S))) as dependent variables were modeled by a multivariate mixed model to same covariates in order to have precise estimations with high power and appropriate interpretation of covariate effects. The analysis was carried out with SAS Proc MIXED and STATA software. Results: A total of 32 studies from the published literature were analysed, covering 4,092 patients who received anthracycline-based and 2,501 treated with CMF for the disease free survival and in order to analyze the overall survival, 13 studies reported the overall survival curves in which 2,050 cases were treated with anthracycline-based and 1,282 with CMF regimens. Conclusions: The findings illustrated that the model with dependent variable Ln (-Ln(S)) had more precise estimations of the covariate effects and showed significant difference between the effects of two adjuvant chemotherapy regimens. Anthracycline-based treatment gave better disease free survival and overall survival. As an IPD meta-analysis in the Italy the results of Angelo et al in 2011 also confirmed that anthracycline-based regimens were more effective for survival of breast cancer patients. The findings of Zare et al 2012 on disease free survival curves in Asia also provided similar evidence.

• 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.

• 한국초전도ㆍ저온공학회논문지
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• 제7권2호
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• pp.21-26
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• 2005

Breakdown characteristics and survival probability of turn-to-turn models were investigated under ac and impulse voltage at 77K. For experiments, two test electrode models were fabricated: One is point contact model and the other is surface contact model. Both are made of copper wrapped by O.025mm thick polyimide film(Kapton). The experimental results were analyzed statistically using Weibull distribution in order to examine the wrapping number effects on voltage-time characteristics under ac voltage as well as under impulse voltage in LN$_{2}$. Also survival analysis were performed according to the Kaplan-Meier method. The breakdown voltages of surface contact model are lower than that of point contact model, because the contact area of surface contact model is wider than that of point contact model. Besides, the shape parameter of point contact model is a little bit larger than that of surface contact model. The time to breakdown t$_{50}$ is decreased as the applied voltage is increased, and the lifetime indices slightly are increased as the number of layers is increased. According to the increasing applied voltage and decreasing wrapping number, the survival probability is increased.

• 한국농공학회:학술대회논문집
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• 한국농공학회 2005년도 학술발표논문집
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• pp.456-460
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• 2005

The purpose of this study is to develop a estimation model of migration with only population data using the cohort-survival model which has been used for forecasting future population. The fluctuation of population can be bisected to the natural change which can be occurred by birth and death and the social change which means migration. The factors of the social change are usually very important for establishing rural policies. However, researches using migration data has limitations because the usage of them are restricted. For verifying a estimation model of migration, comparing estimated population in 2000 year and migration quantity between 1996 and 2000 of 25 gu with real values, using population data and death ratio from 1995 to 2000 of the 25 gu in Seoul. Result shows a reliable data that R-square of forecating population model is 0.9755 and migration is 0.9180. So these model are worth to estimate a population and migration quantity to restricted migration data.

• Asian Pacific Journal of Cancer Prevention
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• 제16권18호
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• pp.8567-8571
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• 2016

Background: The Cox model is known as one of the most frequently-used methods for analyzing survival data. However, in some situations parametric methods may provide better estimates. In this study, a Weibull parametric model was employed to assess possible prognostic factors that may affect the survival of patients with breast cancer. Materials and Methods: We studied 438 patients with breast cancer who visited and were treated at the Cancer Research Center in Shahid Beheshti University of Medical Sciences during 1992 to 2012; the patients were followed up until October 2014. Patients or family members were contacted via telephone calls to confirm whether they were still alive. Clinical, pathological, and biological variables as potential prognostic factors were entered in univariate and multivariate analyses. The log-rank test and the Weibull parametric model with a forward approach, respectively, were used for univariate and multivariate analyses. All analyses were performed using STATA version 11. A P-value lower than 0.05 was defined as significant. Results: On univariate analysis, age at diagnosis, level of education, type of surgery, lymph node status, tumor size, stage, histologic grade, estrogen receptor, progesterone receptor, and lymphovascular invasion had a statistically significant effect on survival time. On multivariate analysis, lymph node status, stage, histologic grade, and lymphovascular invasion were statistically significant. The one-year overall survival rate was 98%. Conclusions: Based on these data and using Weibull parametric model with a forward approach, we found out that patients with lymphovascular invasion were at 2.13 times greater risk of death due to breast cancer.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2021년도 추계학술발표대회
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• pp.779-782
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• 2021

Survival time analysis is one of the main methods used by the pathologist to prognosis for cancer patients. In this paper, we strive to estimate the individual survival time of Adenocarcinoma (ADC) lung cancer patients from pathological images by adopting the convolutional neural network called the SurvPatchV1 model. First, we extracted tissue patches from the whole-slide images (WSI) to deal with extremely large dimensions of WSI. Then the survival time of each patch is estimated through the SurvPatchV1 model. Finally, the individual survival time of each patient is computed. The proposed method is trained and tested on the subset of the NLST dataset for ADC lung cancer. The result demonstrates that our model can obtain all tissue information in lieu of only tumor information in a whole pathological image to estimate the individual survival time.

• Journal of the Korean Data and Information Science Society
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• 제15권2호
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• pp.405-411
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• 2004

In the analysis of cancer data, it is important to make inferences of survival function and to assess the effects of covariates. Cox's proportional hazard model(PHM) and Beran's nonparametric method are generally used to estimate the survival function with covariates. We adjusted the incomplete survival time using the Buckley and James's(1979) pseudo random variables, and then proposed the estimator for the conditional survival function. Also, we carried out the simulation studies to compare the performances of the proposed method.

• Asian Pacific Journal of Cancer Prevention
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• 제17권3호
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• pp.1193-1196
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• 2016

Background: Colorectal cancer (CRC) is the commonest malignancy in the lower gastrointestinal tract in both men and women. It is the third leading cause of cancer-dependent death in the world. In Iran the incidence of colorectal cancer has increased during the last 25 years. Materials and Methods: In this article we analyzed the survival of 447 colorectal patients of Taleghani hospital in Tehran using parametric competing-risks models. The cancers of these patients were diagnosed during 1985 - 2012 and followed up to 2013. The purpose was to assess the association between survival of patients with colorectal cancer in the presence of competing-risks and prognostic factors using parametric models. The analysis was carried out using R software version 3.0.2. Results: The prognostic variables included in the model were age at diagnosis, tumour site, body mass index and sex. The effect of age at diagnosis and body mass index on survival time was statistically significant. The median survival for Iranian patients with colorectal cancer is about 20 years. Conclusions: Survival function based on Weibull model compared with Kaplan-Meier survival function is smooth. Iranian data suggest a younger age distribution compared to Western reports for CRC.