• Journal of the Korean Data and Information Science Society
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• v.13 no.2
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• pp.31-38
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• 2002

In the case of multiple survival times which might be censored at each covariate vector, we study the regression quantile estimations in this paper. The estimations are based on the empirical distribution functions of the censored times and the sample quantiles of the observed survival times at each covariate vector and the weighted least square method is applied for the estimation of the regression quantile. The estimators are shown to be asymptotically normally distributed under some regularity conditions.

• Journal of the Korean Data and Information Science Society
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• v.20 no.5
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• pp.933-939
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• 2009

The bivariate data in clinical research fields often has two types of failure times, which are mark variable for the first failure time and the final failure time. This paper showed how to generate bootstrap data to get Bayesian estimation for the joint distribution of bivariate survival times. The observed data was generated by Frank's family and the fake date is simulated with the Gamma prior of survival time. The bootstrap data was obtained by combining the mimic data with the observed data and the simulated fake data from the observed data.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.11
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• pp.6899-6903
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• 2013

Background: Prevalence is a statistic of primary interest in public health. In the absence of good follow-up facilities, it is difficult to assess the complete prevalence of cancer for a given registry area. Objective: An attempt was here made to arrive at complete prevalence including limited duration prevalence with respect to selected sites of cancer for India by fitting appropriate models to 1, 3 and 5 years cancer survival data available for selected population-based registries. Materials and Methods: Survival data, available for the registries of Bhopal, Chennai, Karunagappally, and Mumbai was pooled to generate survival for breast, cervix, ovary, lung, stomach and mouth cancers. With the available data on survival for 1, 3 and 5 years, a model was fitted and the survival curve was extended beyond 5 years (up to 35 years) for each of the selected sites. This helped in generation of survival proportions by single year and thereby survival of cancer cases. With the help of survival proportions available year-wise and the incidence, prevalence figures were arrived for selected cancer sites and for selected periods. Results: The prevalence to incidence ratio (PI ratio) stabilized after a certain duration for all the cancer sites showing that from the knowledge of incidence, the prevalence can be calculated. The stabilized P/I ratios for the cancer sites of breast, cervix, ovary, stomach, lung, mouth and for life time was observed to be 4.90, 5.33, 2.75, 1.40, 1.37, 4.04 and 3.42 respectively. Conclusions: The validity of the model approach to calculate prevalence could be demonstrated with the help of survival data of Barshi registry for cervix cancer, available for the period 1988-2006.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.2
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• pp.663-669
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• 2014

Background: With recent progress in health science administration, a huge amount of data has been collected from thousands of subjects. Statistical and computational techniques are very necessary to understand such data and to make valid scientific conclusions. The purpose of this paper was to develop a statistical probability model and to predict future survival times for male breast cancer patients who were diagnosed in the USA during 1973-2009. Materials and Methods: A random sample of 500 male patients was selected from the Surveillance Epidemiology and End Results (SEER) database. The survival times for the male patients were used to derive the statistical probability model. To measure the goodness of fit tests, the model building criterions: Akaike Information Criteria (AIC), Bayesian Information Criteria (BIC), and Deviance Information Criteria (DIC) were employed. A novel Bayesian method was used to derive the posterior density function for the parameters and the predictive inference for future survival times from the exponentiated Weibull model, assuming that the observed breast cancer survival data follow such type of model. The Markov chain Monte Carlo method was used to determine the inference for the parameters. Results: The summary results of certain demographic and socio-economic variables are reported. It was found that the exponentiated Weibull model fits the male survival data. Statistical inferences of the posterior parameters are presented. Mean predictive survival times, 95% predictive intervals, predictive skewness and kurtosis were obtained. Conclusions: The findings will hopefully be useful in treatment planning, healthcare resource allocation, and may motivate future research on breast cancer related survival issues.

• Journal of the Korea Institute of Military Science and Technology
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• v.27 no.1
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• pp.94-106
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• 2024

In this study, we conducted a survival analysis on battalion-level commanders(leaders), focusing on infantry battalion defensive operations using the big data of brigade-level KCTC(Korea Combat Training Center) training results. Unlike previous studies, we utilized the brigade-level KCTC training results data for the first time to conduct a survival analysis, and the research subjects were battalion-level commanders(leaders), which can affect the battle. At this time, the battle results were defined, and through cluster analysis, infantry battalions were divided into excellent, average, and insufficient units, and the difference in the survival rate of the commanders was analyzed through the Kaplan-Meier survival analysis. This provided an opportunity to objectively compare the differences between excellent and insufficient units. Subsequently, factors affecting the survival of commanders were derived using the Cox proportional hazard model, and it was possible to confirm the influencing factors from various angles by also using the survival tree model. Significance and limitations confirmed in the research process were presented as policy suggestions and future research directions.

• The Korean Journal of Applied Statistics
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• v.20 no.1
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• pp.39-52
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• 2007

Crossover trials of new drugs in the treatment of angina pectoris, which frequently use treadmill exercise test for the assessment of its efficacy, produce censored survival times. In this paper we consider analysis approaches for censored survival times from crossover trials. Previously, a stratified Cox model for paired observation and nonparametric methods have been presented as possible analysis methods. On the other hand, the differences of two survival times would produce interval-censored survival times and we propose a Cox model for interval-censored data as n alternative analysis method. Example data is analyzed in order to compare these different methods.

• Communications for Statistical Applications and Methods
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• v.26 no.5
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• pp.445-461
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• 2019

It is possible that data are not always fitted with sufficient precision by the existing distributions; therefore this article presents a methodology that enables the use of families of asymmetric distributions as alternative probabilistic models for survival analysis, with censorship on the right, different from those usually studied (the Exponential, Gamma, Weibull, and Lognormal distributions). We use a more flexible parametric model in terms of density behavior, assuming that data can be fit by a distribution of stable distribution families considered unconventional in the analyses of survival data that are appropriate when extreme values occur, with small probabilities that should not be ignored. In the methodology, the determination of the analytical expression of the risk function h(t) of the $L{\acute{e}}vy$ distribution is included, as it is not usually reported in the literature. A simulation was conducted to evaluate the performance of the candidate distribution when modeling survival times, including the estimation of parameters via the maximum likelihood method, survival function ${\hat{S}}$(t) and Kaplan-Meier estimator. The obtained estimates did not exhibit significant changes for different sample sizes and censorship fractions in the sample. To illustrate the usefulness of the proposed methodology, an application with real data, regarding the survival times of patients with colon cancer, was considered.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.14
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• pp.5681-5684
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• 2014

Objective: Prevalence is a statistic of primary interest in public health. In the absence of good follow-up facilities, it is often difficult to assess the complete prevalence of cancer for a given registry area. An attempt is made to arrive at the complete prevalence including limited duration prevalence with respect of selected sites of cancer for India by fitting appropriate models to 1, 3 and 5 year cancer survival data available for selected registries of India. Methodology: Cancer survival data, available for the registries of Bhopal, Chennai, Karunagappally, and Mumbai was pooled to generate survival for the selected cancer sites. With the available data on survival for 1, 3 and 5 years, a model was fitted and the survival curve was extended beyond 5 years (up to 30 years) for each of the selected sites. This helped in generation of survival proportions by single year and thereby survival of cancer cases. With the help of estimated survived cases available year wise and the incidence, the prevalence figures were arrived for selected cancer sites and for selected periods. In our previous paper, we have dealt with the cancer sites of breast, cervix, ovary, lung, stomach and mouth (Takiar and Jayant, 2013). Results: The prevalence to incidence ratio (PI ratio) was calculated for 30 years duration for all the selected cancer sites using the model approach showing that from the knowledge of incidence and P/I ratio, the prevalence can be calculated. The validity of the approach was shown in our previous paper (Takiar and Jayant, 2013). The P/I ratios for the cancer sites of lip, tongue, oral cavity, hypopharynx, oesophagus, larynx, nhl, colon, prostate, lymphoid leukemia, myeloid leukemia were observed to be 10.26, 4.15, 5.89, 2.81, 1.87, 5.43, 5.48, 5.24, 4.61, 3.42 and 2.65, respectively. Conclusion: Cancer prevalence can be readily estimated with use of survival and incidence data.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.1
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• pp.95-100
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• 2016

Background: Survival time of lymphoma patients can be estimated with the help of microarray technology. In this study, with the use of iterative Bayesian Model Averaging (BMA) method, survival time of Mantle Cell Lymphoma patients (MCL) was estimated and in reference to the findings, patients were divided into two high-risk and low-risk groups. Materials and Methods: In this study, gene expression data of MCL patients were used in order to select a subset of genes for survival analysis with microarray data, using the iterative BMA method. To evaluate the performance of the method, patients were divided into high-risk and low-risk based on their scores. Performance prediction was investigated using the log-rank test. The bioconductor package "iterativeBMAsurv" was applied with R statistical software for classification and survival analysis. Results: In this study, 25 genes associated with survival for MCL patients were identified across 132 selected models. The maximum likelihood estimate coefficients of the selected genes and the posterior probabilities of the selected models were obtained from training data. Using this method, patients could be separated into high-risk and low-risk groups with high significance (p<0.001). Conclusions: The iterative BMA algorithm has high precision and ability for survival analysis. This method is capable of identifying a few predictive variables associated with survival, among many variables in a set of microarray data. Therefore, it can be used as a low-cost diagnostic tool in clinical research.

• Asian Journal of Innovation and Policy
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• v.7 no.1
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• pp.55-78
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• 2018

This paper investigates the linkage between the mode of transformation of entrepreneurial learning into outcomes and the subsequent impact of these learning outcomes in enhancing the survival of high-tech startups in India. The study uses data from 45 high-tech startups headquartered across different locations in India for the purpose of analysis. Survival Analysis of the data is conducted to determine which mode of learning transformation and what type of en trepreneurial decision making preference have a significant influence on the survival of Indian high-tech startups and to what extent do they impact their survival. The results indicate that entrepreneur's prior startup experience, explorative mode of learning transformation, causal decision making of the entrepreneur and availability of funding for the startup as the key factors that reduce the time to survival of Indian high-tech startups. They also provide key insights on how these factors impact the startup survival in this region.