• Genomics & Informatics
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• v.17 no.4
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• pp.41.1-41.12
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• 2019

Survival analysis mainly deals with the time to event, including death, onset of disease, and bankruptcy. The common characteristic of survival analysis is that it contains "censored" data, in which the time to event cannot be completely observed, but instead represents the lower bound of the time to event. Only the occurrence of either time to event or censoring time is observed. Many traditional statistical methods have been effectively used for analyzing survival data with censored observations. However, with the development of high-throughput technologies for producing "omics" data, more advanced statistical methods, such as regularization, should be required to construct the predictive survival model with high-dimensional genomic data. Furthermore, machine learning approaches have been adapted for survival analysis, to fit nonlinear and complex interaction effects between predictors, and achieve more accurate prediction of individual survival probability. Presently, since most clinicians and medical researchers can easily assess statistical programs for analyzing survival data, a review article is helpful for understanding statistical methods used in survival analysis. We review traditional survival methods and regularization methods, with various penalty functions, for the analysis of high-dimensional genomics, and describe machine learning techniques that have been adapted to survival analysis.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.1
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• pp.185-190
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• 2014

Influence of loneliness on human survival has been established epidemiologically, but genomic research remains undeveloped. We identified 34 loneliness-associated genes which were statistically significant for high-lonely and low-lonely individuals. With the univariate Cox proportional hazards regression model, we obtained corresponding regression coefficients for loneliness-associated genes fo individual cancer patients. Furthermore, risk scores could be generated with the combination of gene expression level multiplied by corresponding regression coefficients of loneliness-associated genes. We verified that high-risk score cancer patients had shorter mean survival time than their low-risk score counterparts. Then we validated the loneliness-associated gene signature in three independent brain cancer cohorts with Kaplan-Meier survival curves (n=77, 85 and 191), significantly separable by log-rank test with hazard ratios (HR) >1 and p-values <0.0001 (HR=2.94, 3.82, and 1.78). Moreover, we validated the loneliness-associated gene signature in bone cancer (HR=5.10, p-value=4.69e-3), lung cancer (HR=2.86, p-value=4.71e-5), ovarian cancer (HR=1.97, p-value=3.11e-5), and leukemia (HR=2.06, p-value=1.79e-4) cohorts. The last lymphoma cohort proved to have an HR=3.50, p-value=1.15e-7. Loneliness-associated genes had good survival prediction for cancer patients, especially bone cancer patients. Our study provided the first indication that expression of loneliness-associated genes are related to survival time of cancer patients.

• Bulletin of the Korean Chemical Society
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• v.21 no.1
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• pp.93-96
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• 2000

A diffusion-influenced pseudo-first order reversible reaction A + B ⇔C + B is investigated by the molecular dynamics (MD) simulation method. Theoretical finding that the temporal evolution of reactants [conditional probabilities] in the reversible system can be expressed by the irreversible survival probability with an effective rate parameter is confirmed even in the presence of solvent particles. We carry out molecular dynamics simulations for both the irreversible and the reversible cases to evaluate the survival and the conditional probabilities for each cases. When the resultant irreversible survival probability is inserted into the proposed relation, the conditional probabilities given by the simulation are exactly reproduced.

• Journal of Korea Water Resources Association
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• v.42 no.6
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• pp.457-464
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• 2009

This paper proposes methodologies for analyzing the accuracy of the proportional hazards model in predicting consecutive break times of water mains and estimating the time interval for economical water main replacement. By using the survival functions that are based on the proportional hazards models a criterion for the prediction of the consecutive pipe breaks is determined so that the prediction errors are minimized. The criterion to predict pipe break times are determined as the survival probability of 0.70 and only the models for the third through the seventh break are analyzed to be reliable for predicting break times for the case study pipes. Subsequently, the criterion and the estimated lower and upper bound survival functions of consecutive breaks are used in predicting the lower and upper bounds of the 95% confidence interval of future break times of an example water main. Two General Pipe Break Prediction Models(GPBMs) are estimated for an example pipe using the two series of recorded and predicted lower and upper bound break times. The threshold break rate is coupled with the two GPBMs and solved for time to obtain the economical replacement time interval.

• Journal of Hospice and Palliative Care
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• v.10 no.4
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• pp.171-177
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• 2007

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.14
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• pp.5571-5575
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• 2014

Background: The ability to predict the survival time of breast cancer patients is important because of the potential high morbidity and mortality associated with the disease. To develop a predictive inference for determining the survival of breast cancer patients, we applied a novel Bayesian method. In this paper, we propose the development of a databased statistical probability model and application of the Bayesian method to predict future survival times for White Hispanic female breast cancer patients, diagnosed in the US during 1973-2009. Materials and Methods: A stratified random sample of White Hispanic female patient survival data was selected from the Surveillance Epidemiology and End Results (SEER) database to derive statistical probability models. Four were considered to identify the best-fit model. We used three standard model-building criteria, which included Akaike Information Criteria (AIC), Bayesian Information Criteria (BIC), and Deviance Information Criteria (DIC) to measure the goodness of fit. Furthermore, the Bayesian method was used to derive future survival inferences for survival times. Results: The highest number of White Hispanic female breast cancer patients in this sample was from New Mexico and the lowest from Hawaii. The mean (SD) age at diagnosis (years) was 58.2 (14.2). The mean (SD) of survival time (months) for White Hispanic females was 72.7 (32.2). We found that the exponentiated Weibull model best fit the survival times compared to other widely known statistical probability models. The predictive inference for future survival times is presented using the Bayesian method. Conclusions: The findings are significant for treatment planning and health-care cost allocation. They should also contribute to further research on breast cancer survival issues.

• Journal of Korean Society of Forest Science
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• v.89 no.1
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• pp.49-54
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• 2000

Stand level survival prediction model was developed that incorporated the incidence of fusiform rust(Cronartium quercuum [Berk.] Miyabe ex Shirai f. sp. fusiforme) and allowed the transition of trees from an uninfected stage to an infected stage. The influence of hardwood interspecific competition on the survival of unthinned planted stands of loblolly pine (Pinus taeda L.) was analyzed by using of information from twelve years of tracking a set of permanent plots representing a broad range of plantation parameters. Significant interaction effects between site index and hardwood basal area per acre were revealed in the survival model. Survival of the planted pines decreased with increasing density of hardwood trees per acre and site index as the productivity rating of the forest land. The effects of hardwood trees interspecific competition on loblolly pine tended to show a negative effect on predicted future number of planted pine trees.

• Genomics & Informatics
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• v.16 no.4
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• pp.32.1-32.7
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• 2018

Ovarian cancer is one of the leading causes of cancer-related deaths in gynecological malignancies. Over 70% of ovarian cancer cases are high-grade serous ovarian cancers and have high death rates due to their resistance to chemotherapy. Despite advances in surgical and pharmaceutical therapies, overall survival rates are not good, and making an accurate prediction of the prognosis is not easy because of the highly heterogeneous nature of ovarian cancer. To improve the patient's prognosis through proper treatment, we present a prognostic prediction model by integrating high-dimensional RNA sequencing data with their clinical data through the following steps: gene filtration, pre-screening, gene marker selection, integrated study of selected gene markers and prediction model building. These steps of the prognostic prediction model can be applied to other types of cancer besides ovarian cancer.

• 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.

• Journal of Navigation and Port Research
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• v.33 no.4
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• pp.255-261
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• 2009

Prediction of the stability for ships is very complex in reality. In this paper, risk based approach is applied to predict the probability of capsize for a certified ship, which is effected by the forces of sea especially the wave loading Safety assessment and risk analysis process are also applied for the probabilistic prediction of stability for ships. The probability of shipsencountering different waves at sea is calculated by the existed statistics data and risk based models. Finally, ship capsizing probability is calculated according to single degree of freedom(SDF) rolling differential equation and basin erosion theory of nonlinear dynamics. Calculation results show that the survival probabilities of ship excited by the forces of the seas, especially in the beam seas status, can be predicted by the risk based method.