• 한국작물학회지
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• 제50권4호
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• pp.276-280
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• 2005

Low temperature is one of the most severe abiotic stress factors limiting growth, productivity and distribution of winter cereals. Reliable field screening method, which can detect small differences in winter survival, is important for the effective selection and development of plants to identity superior cold tolerant winter cereal genotypes. This study was undertaken to provide improved screening method of winter hardiness in the field by increasing the accuracy in evaluating winter hardiness of barley (Hordeum vulgare L.). We introduced furrow in field screening of winter survival. By sowing the plants at the ridge and base, we could minimize the effect of topographic variation in a field by giving higher and lower level of stress at the same time. This method could be used by breeders to conduct accurate evaluation of winter hardiness by selecting the better treatment, which shows close to normal distribution, among the winter survival rate from the ridge, base and mean survival rate of the two in a screening field.

• Maxillofacial Plastic and Reconstructive Surgery
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• 제19권1호
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• pp.79-86
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• 1997

Adenoid cystic carcinoma is characterized by sloe growth, multiple recurrence, a long clinical course and late metastasis. It is less than 1% of all head and neck malignancy, but most prevalent malignancy in salivary gland origin. During 14 years(from 1982 to 1995), 24 patients were diagnosed as adenoid cystic carcinoma in oral and maxillfacial area, in department of Oral and Maxillofacial Surgery, Korea Cancer Center Hospital. We studied clinically about their age and sex distribution, primary site distribution, TNM staging, treatment modalities, overall survival rates, survival rates according to stages. The age range were from 15 years to 79 years, average age weas 51 years. 15 were men and 9 Were women. Maxilla and palate were the most prevalent primary site. The most cases were in stage III(37%) and stage IV(46%). The 3 year and 5 year overall survival rate were 65.0% and 58.5%. The 5 year survival rate of the stage III cases was 66.7%, and that of the stage IV cases was 38.4%.

• Communications for Statistical Applications and Methods
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• 제18권6호
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• pp.787-798
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• 2011

In this paper, we develop some characterization results in terms of survival entropy of the first order statistic. In addition, we generalize the cumulative entropy recently proposed by Di Crescenzo and Logobardi (2009) to a new measure of information (called the failure entropy) and study some properties of it and its dynamic version. Furthermore, power distribution is characterized based on dynamic failure entropy.

• Journal of Periodontal and Implant Science
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• 제35권2호
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• pp.437-448
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• 2005

This study is an analysis of distribution of patients who installed Xive implant in Yonsei University Hospital and types of implant site for about 2 years recall check and cumulative survival rate. 41 implant were used in this study. It shows the conclusion below. 1. Patients at the age of 40s and 50s were 60% of all implant cases and average number of implant was 2.4(man), and 1.9(woman). All cases were operated on mandible, 3 implants on anterior region and 38 implants on posterior region. 2. The major cause of tooth loss is dental caries(48.8%), followed by periodontal disease. 3. Most distribution of bone qaulity for mandibular implant site was type II(65.8%) and bone quantity was type B(75.6%). 4. The majority of implants were those of 11, 13mm in length(95%) and regular diameter in width (64%). 5. The 41(19 persons) Xive implants that were placed in the mandibular anterior and posterior region were all survival and showed a 100% 2 year cumulative survival rate. The results provided us with basic data on patient type, implant distribution, bone condition, and survival rate. We wish that our results coupled with other research data helps assist in the further study for better implant success rates, etc.

• Asian Pacific Journal of Cancer Prevention
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• 제16권17호
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• pp.7923-7927
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• 2015

Background: The Cox PH model is one of the most significant statistical models in studying survival of patients. But, in the case of patients with long-term survival, it may not be the most appropriate. In such cases, a cure rate model seems more suitable. The purpose of this study was to determine clinical factors associated with cure rate of patients with breast cancer. Materials and Methods: In order to find factors affecting cure rate (response), a non-mixed cure rate model with negative binomial distribution for latent variable was used. Variables selected were recurrence cancer, status for HER2, estrogen receptor (ER) and progesterone receptor (PR), size of tumor, grade of cancer, stage of cancer, type of surgery, age at the diagnosis time and number of removed positive lymph nodes. All analyses were performed using PROC MCMC processes in the SAS 9.2 program. Results: The mean (SD) age of patients was equal to 48.9 (11.1) months. For these patients, 1, 5 and 10-year survival rates were 95, 79 and 50 percent respectively. All of the mentioned variables were effective in cure fraction. Kaplan-Meier curve showed cure model's use competence. Conclusions: Unlike other variables, existence of ER and PR positivity will increase probability of cure in patients. In the present study, Weibull distribution was used for the purpose of analysing survival times. Model fitness with other distributions such as log-N and log-logistic and other distributions for latent variable is recommended.

• 대한수학회논문집
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• 제19권1호
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• pp.169-177
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• 2004

For a stationary field $\{X_{\b{j}},\b{j}{\;}\in{\;}{\mathbb{Z}}^d_{+}\}$ of associated random variables with distribution function $F(x)\;=\;P(X_{\b{1}}\;{\leq}\;x)$ we study strong consistency and asymptotic normality of the empirical distribution function, which is proposed as an estimator for F(x). We also consider strong consistency and asymptotic normality of the empirical survival function by applying these results.

• Communications for Statistical Applications and Methods
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• 제8권1호
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• pp.229-235
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• 2001

Let {X$\_$n/, n$\geq$1] be a stationary sequence of negatively associated random variables with distribution function F(x)=P(X$_1$$\leq$x). The empirical distribution function F$\_$n/(x) based on X$_1$, X$_2$,....., X$\_$n/ is proposed as an estimator for F$\_$n/(x). Strong consistency and asymptotic normality of F$\_$n/(x) are studied. We also apply these ideas to estimation of the survival function.

• Communications for Statistical Applications and Methods
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• 제28권5호
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• pp.425-445
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• 2021

A generalization of the log-logistic (LL) distribution called exponentiated log-logistic (ELL) distribution on lines of exponentiated Weibull distribution is considered. In this paper, based on progressive type-II censored samples, we have derived the maximum likelihood estimators and Bayes estimators for three parameters, the survival function and hazard function of the ELL distribution. Then, under the balanced squared error loss (BSEL) and the balanced linex loss (BLEL) functions, their corresponding Bayes estimators are obtained using Lindley's approximation (see Jung and Chung, 2018; Lindley, 1980), Tierney-Kadane approximation (see Tierney and Kadane, 1986) and Markov Chain Monte Carlo methods (see Hastings, 1970; Gelfand and Smith, 1990). Here, to check the convergence of MCMC chains, the Gelman and Rubin diagnostic (see Gelman and Rubin, 1992; Brooks and Gelman, 1997) was used. On the basis of their risks, the performances of their Bayes estimators are compared with maximum likelihood estimators in the simulation studies. In this paper, research supports the conclusion that ELL distribution is an efficient distribution to modeling data in the analysis of survival data. On top of that, Bayes estimators under various loss functions are useful for many estimation problems.

• Communications for Statistical Applications and Methods
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• 제17권1호
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• pp.117-126
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• 2010

Clinical researchers often analyze survival data as binary outcomes using the logistic regression method. This paper examines the information loss resulting from analyzing survival time as binary outcomes. We first demonstrate that, under the proportional hazard assumption, this binary discretization does result in a significant information loss. Second, when fitting a logistic model to survival time data, researchers inadvertently use the maximal statistic. We implement a numerical study to examine the properties of the reference distribution for this statistic, finally, we show that the logistic regression method can still be a useful tool for analyzing survival data in particular when the proportional hazard assumption is questionable.

• Asian Pacific Journal of Cancer Prevention
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• 제15권12호
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• pp.4839-4842
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• 2014

Background: Although the Cox's proportional hazard model is the popular approach for survival analysis to investigate significant risk factors of cancer patient survival, it is not appropriate in the case of log-term disease free survival. Recently, cure rate models have been introduced to distinguish between clinical determinants of cure and variables associated with the time to event of interest. The aim of this study was to use a cure rate model to determine the clinical associated factors for cure rates of patients with breast cancer (BC). Materials and Methods: This prospective cohort study covered 305 patients with BC, admitted at Shahid Faiazbakhsh Hospital, Tehran, during 2006 to 2008 and followed until April 2012. Cases of patient death were confirmed by telephone contact. For data analysis, a non-mixed cure rate model with Poisson distribution and negative binomial distribution were employed. All analyses were carried out using a developed Macro in WinBugs. Deviance information criteria (DIC) were employed to find the best model. Results: The overall 1-year, 3-year and 5-year relative survival rates were 97%, 89% and 74%. Metastasis and stage of BC were the significant factors, but age was significant only in negative binomial model. The DIC also showed that the negative binomial model had a better fit. Conclusions: This study indicated that, metastasis and stage of BC were identified as the clinical criteria for cure rates. There are limited studies on BC survival which employed these cure rate models to identify the clinical factors associated with cure. These models are better than Cox, in the case of long-term survival.