• Asian Pacific Journal of Cancer Prevention
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• v.16 no.17
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• pp.7473-7478
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• 2015

Background: Kermanshahi oil is one the most favorable oils in Iran especially in Kermanshah province. We aimed to evaluate the role of usual intake of Kermanshahi oil and other kinds of dietary fats as well as different meats, vegetables and fruits, carbohydrates, cereals, grains, sweets, candy and lifestyle habits in risk of breast cancer. Materials and Methods: A case-control study with 47 consecutive, newly diagnosed premenopausal breast-cancer patients and 105 age and socioeconomic matched healthy women was conducted from 2013-2014 in Imam Reza hospital of Kermanshah using a standardized, validated questionnaire assessing various anthropometric, socio-demographic, lifestyle and dietary characteristics. Results: Kermanshahi oil intake was associated with a 2.1-fold (OR=2.123, 95% CI 1.332-3.38) (p=0.002) higher likelihood of having breast cancer, while daily intake of other solid animal fats also increased the likelihood by 2.8-fold (OR = 2.754, 95% CI 1.43-5.273) (p < 0.001), after various adjustments made. Lack of fish oil, white meat, vegetables, soy products, nuts and dairy products (especially during adolescence) in daily regimens and lack of sun exposure were significantly associated with premenopausal breast cancer risk in this region. Conclusions: This study suggested that animal fat increases the risk of premenopausal breast cancer but many other dietary and non-dietary factors including calcium and vitamin D deficiency are consistently associated with increased odds of breast cancer in this region.

• Genomics & Informatics
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• v.16 no.4
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• pp.37.1-37.3
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• 2018

Gene-gene interaction is a key factor for explaining missing heritability. Many methods have been proposed to identify gene-gene interactions. Multifactor dimensionality reduction (MDR) is a well-known method for the detection of gene-gene interactions by reduction from genotypes of single-nucleotide polymorphism combinations to a binary variable with a value of high risk or low risk. This method has been widely expanded to own a specific objective. Among those expansions, fuzzy-MDR uses the fuzzy set theory for the membership of high risk or low risk and increases the detection rates of gene-gene interactions. Fuzzy-MDR is expanded by a maximum likelihood estimator as a new membership function in empirical fuzzy MDR (EFMDR). However, EFMDR is relatively slow, because it is implemented by R script language. Therefore, in this study, we implemented EFMDR using RCPP ($c^{{+}{+}}$ package) for faster executions. Our implementation for faster EFMDR, called EMMDR-Fast, is about 800 times faster than EFMDR written by R script only.

• 대한예방의학회:학술대회논문집
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• 1994.02a
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• pp.426-430
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• 1994

The assessment of exposure is an important component of the risk assessment process. Exposure information is used in risk assessment in at least two ways: 1) in the identification of hazards and the epidemiologic research investigating exposure-response relationships and 2) in the development of population exposure estimates. In both of these cases, the value of a chemical risk assessment is enhanced by improvements in the quality of exposure assessments. The optimum exposure assessment is the direct measurement of population exposure; however, such measurements are rarely available. Recent developments in methods for exposure assessment allow estimates to be made that are valid representations of actual exposure. The use of these exposure estimates to classify exposures correctly enhances the likelihood that causal associations between exposure and response will be correctly identified and that population risks will be accurately assessed.

• Journal of the Korean Statistical Society
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• v.33 no.4
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• pp.411-433
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• 2004

The shrinkage preliminary test ridge regression estimators (SPTRRE) based on Wald (W), Likelihood Ratio (LR) and Lagrangian Multiplier (LM) tests for estimating the regression parameters of the multiple linear regression model with multivariate Student's t error distribution are considered in this paper. The quadratic biases and risks of the proposed estimators are compared under both null and alternative hypotheses. It is observed that there is conflict among the three estimators with respect to their risks because of certain inequalities that exist among the test statistics. In the neighborhood of the restriction, the SPTRRE based on LM test has the smallest risk followed by the estimators based on LR and W tests. However, the SPTRRE based on W test performs the best followed by the LR and LM based estimators when the parameters move away from the subspace of the restrictions. Some tables for the maximum and minimum guaranteed efficiency of the proposed estimators have been given, which allow us to determine the optimum level of significance corresponding to the optimum estimator among proposed estimators. It is evident that in the choice of the smallest significance level to yield the best estimator the SPTRRE based on Wald test dominates the other two estimators.

• 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.

• Journal of Radiation Protection and Research
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• v.35 no.1
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• pp.6-11
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• 2010

Embodying the safety of radioactive waste disposal requires the relevant safety criteria and the corresponding stylized methods to demonstrate its compliance with the criteria. This paper proposes a conceptual model of risk-based safety evaluation for integrating complex potential radiation exposure situations in radioactive waste disposal. For demonstrating compliance with a risk constraint, the approach deals with important exposure scenarios from the viewpoint of the receptor to estimate the resulting risk. For respective exposure situations, it considers the occurrence probabilities of the relevant exposure scenarios as their probability of giving rise to doses to estimate the total risk to a representative person by aggregating the respective risks. In this model, an exposure scenario is simply constructed with three components:radionuclide release, radionuclide migration and environment contamination, and interaction between the contaminated media and the receptor. A set of exposure scenarios and the representative person are established from reasonable combinations of the components, based on a balance of their occurrence probabilities and the consequences. In addition, the probability of an exposure scenario is estimated on the assumption that the initiating external factors influence release mechanisms and transport pathways, and its effect on the interaction between the environment and the receptor may be covered in terms of the representative person. This integrated approach enables a systematic risk assessment for complex exposure situations of radioactive waste disposal and facilitates the evaluation of compliance with risk constraints.

• Journal of Korean Academy of Nursing
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• v.47 no.6
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• pp.817-827
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• 2017

Purpose: This study aimed to identify latent classes based on major modifiable risk factors for coronary artery disease. Methods: This was a secondary analysis using data from the electronic medical records of 2,022 patients, who were newly diagnosed with coronary artery disease at a university medical center, from January 2010 to December 2015. Data were analyzed using SPSS version 20.0 for descriptive analysis and Mplus version 7.4 for latent class analysis. Results: Four latent classes of risk factors for coronary artery disease were identified in the final model: 'smoking-drinking', 'high-risk for dyslipidemia', 'high-risk for metabolic syndrome', and 'high-risk for diabetes and malnutrition'. The likelihood of these latent classes varied significantly based on socio-demographic characteristics, including age, gender, educational level, and occupation. Conclusion: The results showed significant heterogeneity in the pattern of risk factors for coronary artery disease. These findings provide helpful data to develop intervention strategies for the effective prevention of coronary artery disease. Specific characteristics depending on the subpopulation should be considered during the development of interventions.

• The Korean journal of helicobacter and upper gastrointestinal research
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• v.18 no.4
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• pp.225-230
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• 2018

Upper gastrointestinal (GI) bleeding (UGIB) is the most common GI emergency, and it is associated with significant morbidity and mortality. Early identification of low-risk patients suitable for outpatient management has the potential to reduce unnecessary costs, and prompt triage of high-risk patients could allow appropriate intervention and minimize morbidity and mortality. Several risk-scoring systems have been developed to predict the outcomes of UGIB. As each scoring system measures different primary outcome variables, appropriate risk scores must be implemented in clinical practice. The Glasgow-Blatchford score (GBS) should be used to predict the need for interventions such as blood transfusion or endoscopic or surgical treatment. Patients with GBS ${\leq}1$ have a low likelihood of adverse outcomes and can be considered for early discharge. The Rockall score was externally validated and is widely used for prediction of mortality. The recently developed AIMS65 score is easy to calculate and was proposed to predict in-hospital mortality. The Forrest classification is based on endoscopic findings and can be used to stratify patients into high- and low-risk categories in terms of rebleeding and thus is useful in predicting the need for endoscopic hemostasis. Early risk stratification is critical in the management of UGIB and may improve patient outcome and reduce unnecessary health care costs through standardization of care.

• Journal of the Korean Society of Safety
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• v.31 no.4
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• pp.120-125
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• 2016

Usually, a risk can be expressed as a product of likelihood and consequence of a hazard factor. Therefore, conventional risk assessment is carried out by frequency analysis and severity analysis, in turns. However, it is well known that intuitive thinking is another excellent way of thinking of human beings. This study aimed to confirm whether there exist any difference in risk assessment results derived by two different procedures - intuitive and analytical. Thus, the present study showed 10 different illustrations to 30 undergraduate students. Their responses were organized as fuzzy membership functions, and summarized as risk assessments, and compared. The results were also verified with the help of statistical hypothesis testing, which showed no significant difference. On the contrary, however, similarity measure used in fuzzy set theory was not credible as anticipated. Many cases failed to satisfy statistical hypothesis even with similarity measure higher than 0.60 so that only a trend could be accepted. In addition, a subject showed a somewhat consistent logical discrepancy in his response, which implied the necessity of sincere analysis in fuzzy formulations.

• The Korean Journal of Applied Statistics
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• v.29 no.1
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• pp.171-180
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• 2016

Basel committee suggests using Value-at-Risk (VaR) and expected shortfall (ES) as a measurement for market risk. Various estimation methods of VaR and ES have been studied in the literature. This paper compares semi-parametric methods, such as conditional autoregressive value at risk (CAViaR) and conditional autoregressive expectile (CARE) methods, and a Gaussian quasi-maximum likelihood estimator (QMLE)-based method through back-testing methods. We use unconditional coverage (UC) and conditional coverage (CC) tests for VaR, and a bootstrap test for ES to check the adequacy. A real data analysis is conducted for S&P 500 index and Hyundai Motor Co. stock price index data sets.