• 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 Korean Data and Information Science Society
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• v.14 no.4
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• pp.975-982
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• 2003

In this paper we considered the problem of estimating the bivariate survival distribution of the random vector (X, Y) when Y may be subject to random censoring but X is always uncensored. Adapting conditional Koziol-Green model, simplified estimator for bivariate survival function is proposed. We perform simulation to compare the proposed estimator with popular estimators and discussed the performance of it.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.3
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• pp.2013-2017
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• 2013

Background: To compare the effects of two adjuvant chemotherapy regimens, anthracycline-based and cyclophosphamide, methotrexate, fluorourical (CMF) on disease free survival for breast cancer patients in the Eastern Mediterranean region and Asia. Methods: In a systematic review with a multivariate mixed model meta-analysis, the reported survival proportion at multiple time points in different studies were combined. Our data sources were studies linking the two chemotherapy regimens on an adjuvant basis with disease free survival published in English and Persian in the Eastern Mediterranean region and Asia. All survival curves were generated with Graphdigitizer software. Results: 14 retrospective cohort studies were located from electronic databases. We analyzed data for 1,086 patients who received anthracycline-based treatment and 1,109 given CMF treatment. For determination of survival proportions and time we usesb the transformation Ln (-Ln(S)) and Ln (time) to make precise estimations and then fit the model. All analyses were carried out with STATA software. Conclusions: Our findings showed a significant efficacy of anthracycline-based adjuvant therapy regarding disease free survival of breast cancer. As a limitation in this meta-analysis we used studies with different types of anthracycline-based regimens.

• Genomics & Informatics
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• v.20 no.2
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• pp.23.1-23.9
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• 2022

A survival prediction model has recently been developed to evaluate the prognosis of resected nonmetastatic pancreatic ductal adenocarcinoma based on a Cox model using two nationwide databases: Surveillance, Epidemiology and End Results (SEER) and Korea Tumor Registry System-Biliary Pancreas (KOTUS-BP). In this study, we applied two machine learning methods-random survival forests (RSF) and support vector machines (SVM)-for survival analysis and compared their prediction performance using the SEER and KOTUS-BP datasets. Three schemes were used for model development and evaluation. First, we utilized data from SEER for model development and used data from KOTUS-BP for external evaluation. Second, these two datasets were swapped by taking data from KOTUS-BP for model development and data from SEER for external evaluation. Finally, we mixed these two datasets half and half and utilized the mixed datasets for model development and validation. We used 9,624 patients from SEER and 3,281 patients from KOTUS-BP to construct a prediction model with seven covariates: age, sex, histologic differentiation, adjuvant treatment, resection margin status, and the American Joint Committee on Cancer 8th edition T-stage and N-stage. Comparing the three schemes, the performance of the Cox model, RSF, and SVM was better when using the mixed datasets than when using the unmixed datasets. When using the mixed datasets, the C-index, 1-year, 2-year, and 3-year time-dependent areas under the curve for the Cox model were 0.644, 0.698, 0.680, and 0.687, respectively. The Cox model performed slightly better than RSF and SVM.

• 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.

• Communications for Statistical Applications and Methods
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• v.25 no.5
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• pp.523-544
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• 2018

Bivariate distributions play a fundamental role in survival and reliability studies. We consider a regression model for bivariate survival times under right-censored based on the bivariate Kumaraswamy Weibull (Cordeiro et al., Journal of the Franklin Institute, 347, 1399-1429, 2010) distribution to model the dependence of bivariate survival data. We describe some structural properties of the marginal distributions. The method of maximum likelihood and a Bayesian procedure are adopted to estimate the model parameters. We use diagnostic measures based on the local influence and Bayesian case influence diagnostics to detect influential observations in the new model. We also show that the estimates in the bivariate Kumaraswamy Weibull regression model are robust to deal with the presence of outliers in the data. In addition, we use some measures of goodness-of-fit to evaluate the bivariate Kumaraswamy Weibull regression model. The methodology is illustrated by means of a real lifetime data set for kidney patients.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.4
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• pp.1533-1537
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• 2012

In general, breast cancer is the most common malignancy among women in developed as well as some developing countries, often being the second leading cause of cancer mortality after lung cancer. Using a parametric log-logistic model to consider the effects of prognostic factors, the present study focused on the 5-year survival of women with the diagnosis of breast cancer in Southern Iran. A total of 1,148 women who were diagnosed with primary invasive breast cancer from January 2001 to January 2005 were included and divided into three prognosis groups: poor, medium, and good. The survival times as well as the hazard rates of the three different groups were compared. The log-logistic model was employed as the best parametric model which could explain survival times. The hazard rates of the poor and the medium prognosis groups were respectively 13 and 3 times greater than in the good prognosis group. Also, the difference between the overall survival rates of the poor and the medium prognosis groups was highly significant in comparison to the good prognosis group. Use of the parametric log-logistic model - also a proportional odds model - allowed assessment of the natural process of the disease based on hazard and identification of trends.

• International Journal of Fluid Machinery and Systems
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• v.5 no.4
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• pp.143-151
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• 2012

Predicting the future condition of machine and assessing the remaining useful life are the center of prognostics. This paper contributes a new prognostic method based on grey model and survival probability. The first step of the method is building a normal condition model then determining the error indicator. In the second step, the survival probability value is obtained based on the error indicator. Finally, grey model coupled with one-step-ahead forecasting technique are employed in the last step. This work has developed a modified grey model in order to improve the accuracy of prediction. For evaluating the proposed method, real trending data of low methane compressor acquired from condition monitoring routine were employed.

• 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.15 no.12
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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.