• Food Science and Biotechnology
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• 제18권1호
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• pp.137-142
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• 2009

One of the most important aspects of conducting this microbial risk assessment (MRA) is determining the model in microbial behaviors in food systems. However, to fully these modeling, large expenditures or newly laboratory experiments will be spent to do it. To overcome these problems, it has to be considered to develop the new strategies that can be used data in the published literatures. This study is to show whether or not the data set from the published experimental data has more value for modeling for MRA. To illustrate this suggestion, as example of data set, 4 published Salmonella survival in Cheddar cheese reports were used. Finally, using the GInaFiT tool, survival was modeled by nonlinear polynomial regression model describing the effect of temperature on Weibull model parameters. This model used data in the literatures is useful in describing behavior of Salmonella during different time and temperature conditions of cheese ripening.

• Communications for Statistical Applications and Methods
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• 제4권2호
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• pp.491-496
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• 1997

For the survival data analysis of no covariate the discount survival model is proposed to estimate the time-varying hazard rate and the survival function recursively. In comparison with the covariate case it provide the distributionally explicit evolution of hazard rate between time intervals under the assumption of a conjugate gamma distribution. Also forecasting of the hazard rate in the next time interval is suggested, which leads to the forcecasted survival function.

• 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.

• 한국통계학회:학술대회논문집
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• 한국통계학회 2000년도 추계학술발표회 논문집
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• pp.193-200
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• 2000

Modelling the dependence via random effects in censored multivariate survival data has recently received considerable attention in the biomedical literature. The random effects models model not only the conditional survival times but also the conditional hazard rate. Systematic likelihood inference for the models with random effects is possible using Lee and Nelder's (1996) hierarchical-likelihood (h-likelihood). The purpose of this presentation is to introduce Ha et al.'s (2000a,b) inferential methods for the random effects models via the h-likelihood, which provide a conceptually simple, numerically efficient and reliable inferential procedures.

• 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.

• Asian Pacific Journal of Cancer Prevention
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• 제17권7호
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• pp.3575-3581
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• 2016

Background: Cancer survival analysis is an essential indicator for effective early detection and improvements in cancer treatment. This study was undertaken to document colorectal cancer survival and associated prognostic factors in Malaysians. Materials and Methods: All data were retrieved from the National Cancer Patient Registry-Colorectal Cancer. Only cases with confirmed diagnosis through histology between the year 2008 and 2009 were included. Retrieved data include socio-demographic information, pathological features and treatment received. Survival curves were plotted using the Kaplan-Meier method. Univariate analysis of all variables was then made using the Log-rank test. All significant factors that influenced survival of patients were further analysed in a multivariate analysis using Cox' regression. Results: Total of 1,214 patients were included in the study. The overall 3- and 5-year survival rates were 59.1% and 48.7%, respectively. Patients with localized tumours had better prognosis compared to those with advanced stage cancer. In univariate analysis, staging at diagnosis (p<0.001), primary tumour size (p<0.001), involvement of lymph nodes (p<0.001) and treatment modalities (p=0.001) were found to be predictors of survival. None of the socio-demographic characteristics were found to exert any influence. In Cox regression analysis, staging at diagnosis (p<0.001), primary tumour size (p<0.001), involvement of lymph nodes (p<0.001) and treatment modalities (p<0.001) were determined as independent prognostic factors of survival after adjusted for age, gender and ethnicity. Conclusions: The overall survival rate for colorectal cancer patients in Malaysia is similar to those in other Asian countries, with staging at diagnosis, primary tumor size, involvement of lymph node and treatment modalities having significant effects. More efforts are needed to improve national survival rates in future.

• Asian Pacific Journal of Cancer Prevention
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• 제14권1호
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• pp.159-163
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• 2013

Background: This study used Surveillance, Epidemiology and End Results (SEER) pancreatic cancer data to identify predictive models and potential socio-economic disparities in pancreatic cancer outcome. Materials and Methods: For risk modeling, Kaplan Meier method was used for cause specific survival analysis. The Kolmogorov-Smirnov's test was used to compare survival curves. The Cox proportional hazard method was applied for multivariate analysis. The area under the ROC curve was computed for predictors of absolute risk of death, optimized to improve efficiency. Results: This study included 58,747 patients. The mean follow up time (S.D.) was 7.6 (10.6) months. SEER stage and grade were strongly predictive univariates. Sex, race, and three socio-economic factors (county level family income, rural-urban residence status, and county level education attainment) were independent multivariate predictors. Racial and socio-economic factors were associated with about 2% difference in absolute cause specific survival. Conclusions: This study s found significant effects of socio-economic factors on pancreas cancer outcome. These data may generate hypotheses for trials to eliminate these outcome disparities.

• Journal of the Korean Data and Information Science Society
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• 제26권3호
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• pp.569-580
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• 2015

두 집단의 생존분포의 비교를 위한 방법으로는 로그-순위 검정법이 널리 쓰이고 있다. 그러나 이는 두 집단의 생존분포 간에 비례위험모형 가정이 성립하는 경우에 적합한 것으로써, 두 집단의 생존분포가 교차하는 상황 하에서는 해당 검정법의 유효성을 장담할 수 없다. 그러나 두 집단의 생존분포가 교차하는 상황은 빈번히 발생하며, 이 같은 상황에서 두 생존분포의 동일성 검정을 위한 여러 연구가 진행되어왔다. 본 논문에서는 두 집단 간 생존분포가 교차하는 상황 하에서의 동일성 검정을 위한 방법들을 고려한다. 나아가 교차하는 상황을 위치에 따라 세분화하고 모의실험을 통해 각 상황에서 다양한 검정법들의 검정력을 비교하였으며, 그 결과를 토대로 주어진 상황에서 적절한 방법의 선택에 유용한 정보를 제공하고자 한다.

• 응용통계연구
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• 제23권3호
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• pp.521-531
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• 2010

구간중도절단은 중도절단의 가장 일반적인 개념으로 구간중도절단자료는 의학 및 역학분야의 연구에서 흔히 관찰된다. 본 연구에서는 구간중도절단의 상황에서 생존함수와 중간생존시간을 추정하는 방법으로 평균대치법과 자기일치법을 비교 연구하고, 실제 자료로 혈우병환자에서 선천성면역결핍바이러스 감염시점을 추정하였다. 또한 구간중도절단자료를 생성하는 새로운 방법을 제시하였으며, 생성된 구간중도절단자료를 이용한 모의실험을 통하여 두 추정치에 대한 다양한 비교연구를 시행하였다. 구간중도절단자료에서 생존함수와 중간생존시간을 추정할 경우 중도절단율이 크지 않다면 평균대치법이 자기일치법보다 더 우수한 추정치로 판명되었다.

• 한국의학물리학회지:의학물리
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• 제28권1호
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• pp.11-15
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• 2017

Relative biological effectiveness (RBE) of particle beam needs to be evaluated at particle beam therapy centers before the clinical application of the particle beam. However, since RBE analysis is implemented manually, it is useful to have a tool that can easily and effectively handle the data of experiments to generate cell survival curve and to analyze RBE simultaneously. In this work, the development of a program for RBE analysis of particle beam therapy was presented. This RBE analysis program was developed to include two parts; fitting the cell survival curves to linear-quadratic model and calculating the RBE values at a certain endpoint using fitting results. This program was also developed to simultaneously compare and analyze the template results that stored experiment data with photon and particle beam irradiations. The results of the cell survival curve obtained by each irradiation can be analyzed by the user on a desired data after reading the template stored in the easy-to-use excel file. The analysis results include the cell survival curves with error range, which are appeared in the screen and the ${\alpha}$ and ${\beta}$ parameters of linear-quadratic model with 95% confidence intervals, RBE values, and $R^2$ values to evaluate goodness-of-fit of survival curves to model, which are stored in a text cvs file. This software can generate cell survival curve, fit to model, and calculate RBE all at once with raw experiment data, so it helps users to save time for data handling and to reduce the possibility of making error on analysis. As a coming plan, we will create a user-friendly graphical user interface to present the results more intuitively.