• Asian Pacific Journal of Cancer Prevention
• /
• v.13 no.8
• /
• pp.4045-4050
• /
• 2012

Objective: The impact of ablative oral cancer surgery was studied, with particular reference to recurrence and nodal metastasis, to assess survival probability and prognostic indicators and to elucidate if ethnicity influences the survival of patients. Methods: Patients who underwent major ablative surgery of the head and neck region with neck dissection were identified and clinical records were assessed. Inclusion criteria were stage I-IV oral and oropharyngeal malignancies necessitating resection with or without radiotherapy from 2004 to 2009. All individuals had a pre-operative assessment prior to the surgery. The post operative assessment period ranged from 1 year to 5 years. Survival distributions were analyzed using Kaplan-Meier curves. Results: 87 patients (males:38%; females:62%) were included in this study, with an age range of 21-85 years. Some 78% underwent neck dissections while 63% had surgery and radiotherapy. Nodal recurrence was detected in 5.7% while 20.5% had primary site recurrence within the study period. Kaplan-Meier survival analysis revealed that the median survival time was 57 months. One year overall survival (OS) rate was 72.7% and three year overall survival rate dropped to 61.5%. On OS analysis, the log-rank test showed a significant difference of survival between Malay and Chinese patients (Bonferroni correction p=0.033). Recurrence-free survival (RFS) analysis revealed that 25% of the patients have reached the event of recurrence at 46 months. One year RFS rate was 85.2% and the three year survival rate was 76.1%. In the RFS analysis, the log-rank test showed a significant difference in the event of recurrence and nodal metastasis (p<0.001). Conclusion: Conservative neck is effective, in conjunction with postoperative radiotherapy, for control of neck metastases. Ethnicity appears to influence the survival of the patients, but a prospective trial is required to validate this.

• Asian Pacific Journal of Cancer Prevention
• /
• v.15 no.19
• /
• pp.8371-8376
• /
• 2014

Background: The use of statistical methods has become an imperative tool in breast cancer survival data analysis. The purpose of this study was to develop the best statistical probability model using the Bayesian method to predict future survival times for the black non-Hispanic female breast cancer patients diagnosed during 1973-2009 in the U.S. Materials and Methods: We used a stratified random sample of black non-Hispanic female breast cancer patient data from the Surveillance Epidemiology and End Results (SEER) database. Survival analysis was performed using Kaplan-Meier and Cox proportional regression methods. Four advanced types of statistical models, Exponentiated Exponential (EE), Beta Generalized Exponential (BGE), Exponentiated Weibull (EW), and Beta Inverse Weibull (BIW) were utilized for data analysis. The statistical model building criteria, Akaike Information Criteria (AIC), Bayesian Information Criteria (BIC), and Deviance Information Criteria (DIC) were used to measure the goodness of fit tests. Furthermore, we used the Bayesian approach to obtain the predictive survival inferences from the best-fit data based on the exponentiated Weibull model. Results: We identified the highest number of black non-Hispanic female breast cancer patients in Michigan and the lowest in Hawaii. The mean (SD), of age at diagnosis (years) was 58.3 (14.43). The mean (SD), of survival time (months) for black non-Hispanic females was 66.8 (30.20). Non-Hispanic blacks had a significantly increased risk of death compared to Black Hispanics (Hazard ratio: 1.96, 95%CI: 1.51-2.54). Compared to other statistical probability models, we found that the exponentiated Weibull model better fits for the survival times. By making use of the Bayesian method predictive inferences for future survival times were obtained. Conclusions: These findings will be of great significance in determining appropriate treatment plans and health-care cost allocation. Furthermore, the same approach should contribute to build future predictive models for any health related diseases.

• The Korean Journal of Applied Statistics
• /
• v.29 no.7
• /
• pp.1459-1473
• /
• 2016

Both longitudinal data and survival data are collected simultaneously in longitudinal data which are observed throughout the passage of time. In this case, the effect of the independent variable becomes biased (provided that sole use of longitudinal data analysis does not consider the relation between both data used) if the missing that occurred in the longitudinal data is non-ignorable because it is caused by a correlation with the survival data. A joint model of longitudinal data and survival data was studied as a solution for such problem in order to obtain an unbiased result by considering the survival model for the cause of missing. In this paper, a joint model of the longitudinal zero-inflated count data and survival data is studied by replacing the longitudinal part with zero-inflated count data. A hurdle model and proportional hazards model were used for each longitudinal zero inflated count data and survival data; in addition, both sub-models were linked based on the assumption that the random effect of sub-models follow the multivariate normal distribution. We used the EM algorithm for the maximum likelihood estimator of parameters and estimated standard errors of parameters were calculated using the profile likelihood method. In simulation, we observed a better performance of the joint model in bias and coverage probability compared to the separate model.

• Journal of the Korean Operations Research and Management Science Society
• /
• v.39 no.4
• /
• pp.51-69
• /
• 2014

Times to multiple events (TMEs) are a major data type in large-scale business and medical data. Despite its importance, the analysis of TME data has not been well studied because of the analysis difficulty from censoring of observation. To address this difficulty, we have developed a Bayesian-based multivariate survival analysis method, which can successfully estimate the joint probability density of survival times. In this work, we extended this method for the analysis of precedence, dependency and causality among multiple events. We applied this method to the electronic health records of 2,111 patients in a children's hospital in the US and the proposed analysis successfully shows the relation between times to two types of hospital visits for different medical issues. The overall result implies the usefulness of the multivariate survival analysis method in large-scale big data in a variety of areas including marketing, human resources, and e-commerce. Lastly, we suggest our future research directions based multivariate survival analysis method.

• Asian Pacific Journal of Cancer Prevention
• /
• v.15 no.15
• /
• pp.6253-6255
• /
• 2014

Background: Colorectal cancer (CRC) is considered to be a main cause of malignancy-related death in the world, being commonly diagnosed in both men and women. It is the third leading cause of cancer dependent death in the world and there are one million new cases diagnosed per year. In Iran the incidence of colorectal cancer has increased during the last 25 years and it is the fifth cause of cancer in men and the third in women. Materials and Methods: In this article we analyzed the survival of 475 colorectal patients of Taleghani hospital in Tehran with the semi-parametric competing-risks model. Results: There were 55% male cases and at the time of the diagnosis most of the patients were between 48 and 67years old. The probability of a patient death from colorectal cancer with survival of more than 25 years was about 0.4. Body mass index, height, tumour site and gender had no influence. Conclusions: According to these data and by using semi-parametric competing-risks method, we found out that only age at diagnosis has a significant effect on these patient survival time.

• Journal of Korean Institute of Industrial Engineers
• /
• v.42 no.2
• /
• pp.129-137
• /
• 2016

A colonoscopy is important because it detects the presence of polyps in the colon that can lead to colon cancer. How often one needs to repeat a colonoscopy may depend on various factors. The main purpose of this study is to determine personalized surveillance interval of colonoscopy based on characteristics of patients including their clinical information. The clustering analysis using a partitioning around medoids algorithm was conducted on 625 patients who had a medical examination at Korea University Anam Hospital and found several subgroups of patients. For each cluster, we then performed survival analysis that provides the probability of having polyps according to the number of days until next visit. The results of survival analysis indicated that different survival distributions exist among different patients' groups. We believe that the procedure proposed in this study can provide the patients with personalized medical information about how often they need to repeat a colonoscopy.

• Journal of Ocean Engineering and Technology
• /
• v.28 no.1
• /
• pp.69-76
• /
• 2014

Survivability assessments and vulnerability reductions are required in warship design. A warship's survivability is assessed by its susceptibility, vulnerability, and recoverability. In this paper, an integrated survivability assessment for a warship subjected to multiple hits is introduced. The methodology aims at integrating a survivability assessment into an early stage of warship design. The hull surface is idealized using typical geometries for RCS (Radar Cross Section) detection probability and susceptibility. The Vulnerability is evaluated by using the shot-line. The recoverability is estimated using a survival time analysis. This enables the variation of survivability to be assessed. Several parameters may be varied to determine their effects on the survivability. The susceptibility is assessed by the probability of detecting the radar cross section of the subject and the probability of being hit based on a probability density function. The vulnerability is assessed by the kill probability based on the vulnerable area of critical components, according to the component's layout and redundancy. Recoverability is assessed by the recovery time for damaged critical components.

• Journal of the military operations research society of Korea
• /
• v.17 no.1
• /
• pp.159-176
• /
• 1991

The probability of target survival is the most important factor in the target assignment, Most of the studies about it have assumed the case of one target and ane weapon type. Therefore, they can not be applied to the real situation. In this paper. the quantity and type of enemy assets of the friendly force are considered simultaneously. Considered defense type is the coordinated defense with no impact point prediction. The objective function is to minimize the expected total survival value of targets which are scattered in the defense area. The rules of aircraft assignment are as follows : first, classify targets into several groups, each of those has the same desired damage level secondly. select the critical group which has the least survival value in accordance with the additional aircraft assignment, and finally. assign the same number of attack assets against each target in the critical group. In this paper, the attack assets, the escort assets, and the defense assets are considered. The model is useful to not only the simple aircraft assignment problem but also the complicated wargame models.

• The Korean Journal of Applied Statistics
• /
• v.7 no.2
• /
• pp.53-77
• /
• 1994

In a certain stochastic process, Cox's regression model is used to analyze multistate survival data. From this model, the regression parameter vectors, survival functions, and the probability of being in response function are estimated based on multistate Cox's partial likelihood and nonparametric likelihood methods. The asymptotic properties of these estimators are described informally through the counting process approach. An example is given to likelihood the results in this paper.

• Journal of Veterinary Science
• /
• v.24 no.3
• /
• pp.36.1-36.7
• /
• 2023

Platelet to lymphocyte ratio (PLR) is a prognostic marker in human hepatocellular carcinoma (HCC) however, its utility in canine HCC has not been explored. The aim of the study was to determine if PLR could predict survival outcomes in 42 dogs with HCC. PLR was not a significant predictive factor (p = 0.15) but lymphopenia alone was significantly correlated with a reduced probability of survival (p = 0.024). Further studies are needed to evaluate if peripheral lymphocyte count mirrors that of the tumor microenvironment in canine HCC.