• Communications for Statistical Applications and Methods
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• v.7 no.1
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• pp.233-244
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• 2000

Confidence intervals for survival function give useful information about the lifetime distribution. In this paper we develop Edgeworkth expansions as approximation to the true and bootstrap distributions of normalized nonparametric maximum likelihood estimator of survival function in the Koziol-Green model and then use these results to show that the bootstrap approximations have second order accuracy.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.16
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• pp.7359-7363
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• 2015

Background: As a result of significant progress made in treatment of many types of cancers during the last few decades, there have been an increased number of patients who do not experience mortality. We refer to these observations as cure or immune and models for survival data which include cure fraction are known as cure rate models or long-term survival models. Materials and Methods: In this study we used the data collected from 438 female patients with breast cancer registered in the Cancer Research Center in Shahid Beheshti University of Medical Sciences, Tehran, Iran. The patients had been diagnosed from 1992 to 2012 and were followed up until October 2014. We had to exclude some because of incomplete information. Phone calls were made to confirm whether the patients were still alive or not. Deaths due to breast cancer were regarded as failure. To identify clinical, pathological, and biological characteristics of patients that might have had an effect on survival of the patients we used a non-mixture cure rate model; in addition, a Weibull distribution was proposed for the survival time. Analyses were performed using STATA version 14. The significance level was set at $P{\leq}0.05$. Results: A total of 75 patients (17.1%) died due to breast cancer during the study, up to the last follow-up. Numbers of metastatic lymph nodes and histologic grade were significant factors. The cure fraction was estimated to be 58%. Conclusions: When a cure fraction is not available, the analysis will be changed to standard approaches of survival analysis; however when the data indicate that the cure fraction is available, we suggest analysis of survival data via cure models.

• 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.

• Communications for Statistical Applications and Methods
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• v.4 no.2
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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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• v.7 no.1
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• pp.327-336
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• 2000

We are mainly interested in hazard rate changes which are usually occur in survival times of manufactured products or patients. We may expect early failures with one hazard rate and next another hazard rate. For this type of data we apply a hazard rate change-point model and estimate the unkown time point to improve the model adequacy. We introduce change-point logistic model to the discrete time hazard rates. The MLEs are obtained routinely and we also explain the suggested model through a dataset of survival times.

• The Korean Journal of Applied Statistics
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• v.34 no.2
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• pp.255-266
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• 2021

The purpose of this study is to compare the effects of treatment on stage III stomach cancer data obtained from the SEER program of the National Cancer Institute and to identify the significant risk factors for the survival rates of stage III stomach cancer. Since the proportional hazards assumption was violated for treatment, we used the restricted mean survival time as an alternative to the proportional hazards model. The restricted mean survival time was estimated using pseudo-observations, and the effects of treatment were compared using a test statistic based on the estimated restricted mean survival times. We conducted the regression analysis using a generalized linear model to investigate the significant predictors for the restricted mean survival time of patients with stage III stomach cancer. We found that there was a significant difference between the restricted mean survival times of treatment groups. Age at diagnosis, race, substage, grade, tumor size, surgery, and treatment were significant predictors for the restricted mean survival time of patients with stage III stomach cancer. Surgery was the most significant predictor for increasing the restricted mean survival time of patients with stage III stomach cancer.

• Journal of Gastric Cancer
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• v.23 no.4
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• pp.584-597
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• 2023

Purpose: This study aimed to investigate the impact of different types of complications on long-term survival following total gastrectomy for gastric cancer. Materials and Methods: A total of 926 patients who underwent total gastrectomy between 2008 and 2016 were included. Patients were divided into the morbidity and no-morbidity groups, and long-term survival was compared between the 2 groups. The prognostic impact of postoperative morbidity was assessed using a multivariate Cox proportional hazard model, which accounted for other prognostic factors. In the multivariate model, the effects of each complication on survival were analyzed. Results: A total of 229 patients (24.7%) developed postoperative complications. Patients with postoperative morbidity showed significantly worse overall survival (OS) (5-year, 65.0% vs. 76.7%, P<0.001) and cancer-specific survival (CSS) (5-year, 74.2% vs. 83.1%, P=0.002) compared to those without morbidity. Multivariate analysis adjusting for other prognostic factors showed that postoperative morbidity remained an independent prognostic factor for OS (hazard ratio [HR], 1.442; 95% confidence interval [CI], 1.136-1.831) and CSS (HR, 1.463; 95% CI, 1.063-2.013). There was no significant difference in survival according to the severity of complications. The following complications showed a significant association with unfavorable long-term survival: ascites (HR, 1.868 for OS, HR, 2.052 for CSS), wound complications (HR, 2.653 for OS, HR, 2.847 for CSS), and pulmonary complications (HR, 2.031 for OS, HR, 1.915 for CSS). Conclusions: Postoperative morbidity adversely impacted survival following total gastrectomy for gastric cancer. Among the different types of complications, ascites, wound complications, and pulmonary complications exhibited significant associations with long-term survival.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.10
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• pp.4143-4145
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• 2014

Background: This study used receiver operating characteristic curve to analyze Surveillance, Epidemiology and End Results (SEER) Ewing sarcoma (ES) outcome data. The aim of this study was to identify and optimize ES-specific survival prediction models and sources of survival disparities. Materials and Methods: This study analyzed socio-economic, staging and treatment factors available in the SEER database for ES. 1844 patients diagnosed between 1973-2009 were used for this study. For the risk modeling, each factor was fitted by a Generalized Linear Model to predict the outcome (bone and joint specific death, yes/no). The area under the receiver operating characteristic curve (ROC) was computed. Similar strata were combined to construct the most parsimonious models. Results: The mean follow up time (S.D.) was 74.48 (89.66) months. 36% of the patients were female. The mean (S.D.) age was 18.7 (12) years. The SEER staging has the highest ROC (S.D.) area of 0.616 (0.032) among the factors tested. We simplified the 4-layered risk levels (local, regional, distant, un-staged) to a simpler non-metastatic (I and II) versus metastatic (III) versus un-staged model. The ROC area (S.D.) of the 3-tiered model was 0.612 (0.008). Several other biologic factors were also predictive of ES-specific survival, but not the socio-economic factors tested here. Conclusions: ROC analysis measured and optimized the performance of ES survival prediction models. Optimized models will provide a more efficient way to stratify patients for clinical trials.

• Maxillofacial Plastic and Reconstructive Surgery
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• v.35 no.1
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• pp.1-12
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• 2013

Purpose: Vascular endothelial growth factor (VEGF)-C and its tyrosine kinase receptor, VEGF receptor (VEGFR)-3 are recently known to have lymphangiogenic activities in various tumor types. In this study, we determined whether the expression of lymphangiogenic factors correlate with nodal metastasis or survival in a nude mouse model of oral squamous cell carcinoma (OSCC). Methods: Three OSCC cells (KB, SCC4, SCC9) were xenografted into the right mandibular gland of athymic nude mice. The mice were followed for tumor development and growth, and the mice were sacrificed when they had lost more than 20% of their initial body weight, or the diameter of the induced tumor exceeds 20 mm. After necropsy, the murine tumors were examined histologically and radiologically (micro-positron emission tomography computed tomography) for regional or distant metastasis. We performed immunohistochemical assays with anti-VEGF-C, VEGFR-3, CD105, and D2-40 antibodies. Immunofluorescence double staining for LYVE-1/CD31 was also performed. To quantify the VEGF-C and VEGFR-3 level in the cancer tissue, Western blotting was performed. Finally, we determined the correlation between the degree of expression of VEGF-C/VEGFR-3 and the mean survival time. Results: OSCC tumor cells into the mandibular gland of the nude mice successfully resulted in the formation of recapitulating orthotopic tumor. Tumor cells of the induced tumor did not express VEGF-C. VEGF-C/VEGFR-3 expression was mainly distributed in the endothelial cells of the stromal area. There were no correlation between the degree of expression of VEGF-C/VEGFR-3 and the mean survival time of mice injected with different OSCC cell lines. Conclusion: An recapitulating orthotopic model of OSCC in nude mice was established, which copies the cervical nodal metastasis of human OSCC. Overexpression of lymphangiogenic factors seems to have no effect on survival of hosts in this in vivo experiment.

• Progress in Medical Physics
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• v.28 no.1
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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.