• Journal of Animal Science and Technology
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• v.45 no.5
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• pp.725-730
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• 2003

This study was conducted to evaluate the adequacy of an alternative a.m.-p.m. testing scheme for milk yield in comparison with the official test method based on weighing two milkings within 24 h. A total of 8,309 p.m. milking weights and 6,767 a.m. milking weights from 72 Holstein cows raised at N.L.R.I. were collected between October 2000 and November 2001. Ratios were computes for daily milk yield to a.m. and p.m. milking weights(direct yield ratios) and ratios of a.m. and p.m. milking weights to daily milk yield (inverse yield ratios). Analysis of variance indicated that the milking interval is the most important source of variation for yield ratios. Adjustment factors for estimating daily milk yield from single milking weights were derived through regression analysis of direct and inverse yield ratios on the length of the milking interval. Daily milk yield was estimated more precisely and accurately when adjustment factors were used than when single milking weights were doubled. In conclusion, alternative recording of a.m. and p.m. milking weights led to reliable estimates of milk yields.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.1
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• pp.441-447
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• 2013

Background: Prior studies examining the relation between diabetes mellitus (DM) and prostate cancer risk have reported controversial findings. We examined this association by conducting a detailed meta-analysis of the peer-reviewed literature. Methods: A comprehensive search for articles of MEDLINE and EMBASE databases and bibliographies of retrieved articles published up to November, 2012 was performed. Methodological quality assessment of the trials was based on the Newcastle-Ottawa Scaleq and the meta-analysis was performed using STATA 12.0. Dose-response regression was conducted with SPSS 19.0. Results: We included 29 studies in the meta-analysis (13 case-control studies, 16 cohort studies), and found an inverse association between DM and prostate cancer (relative risk (RR) 0.84, 95% confidence interval (CI), 0.78-0.91). An inverse association was also observed in non-Asian populations (RR 0.81, 95% CI 0.76-0.87) and population-based studies (RR 0.80, 95% CI 0.77-0.91). No statistical significance was found of the association between prostate cancer risk and the duration of DM (p=0.338), and risk seemed not related with the age of DM diagnosis. Conclusions: This study suggested an inverse relationship between DM and prostate cancer, but without links to duration of disease or age of diagnosis.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.9
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• pp.4527-4531
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• 2016

Background: PTEN protein is one of the most important tumour suppressor factors which is detectable by immunohistochemistry. The goal of the present study was to investigate the prognostic role of PTEN gene expression in breast cancer patients. Materials and Methods: This descriptive-analytical study was conducted on 100 breast cancer patients referred to Sabzevar hospitals in the north-east of Iran between 2010 and 2011, who were followed up to 2015. PTEN gene expression in tissue samples was determined using specific monoclonal antibodies and data were analyzed using Chi-square test and Fisher's exact test. Patient survival was analyzed after 4 years of follow-up using the Cox regression model. Results: PTEN gene expression was evident in 70 of 100 cnacer samples but was found at high levels in all non-cancer samples. There was an inverse significant relationship between PTEN gene expression and tumour stage or tumour grade (p<0.001). The expression of PTEN in invasive ductal tumours was lower than in non-invasive tumours. There was also an inverse significant relationship between the hazard of death and PTEN gene expression (p<0.001). In addition, there was an inverse significant relationship between tumour stage and hazard of death (p<0.001). Conclusion: These findings indicate that lack of PTEN gene expression can be a sign of a worse prognosis and poor survival in breast cancer cases.

• The Korean Journal of Applied Statistics
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• v.36 no.4
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• pp.323-335
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• 2023

In causal analysis of high dimensional data, it is important to reduce the dimension of covariates and transform them appropriately to control confounders that affect treatment and potential outcomes. The augmented inverse probability weighting (AIPW) method is mainly used for estimation of average treatment effect (ATE). AIPW estimator can be obtained by using estimated propensity score and outcome model. ATE estimator can be inconsistent or have large asymptotic variance when using estimated propensity score and outcome model obtained by parametric methods that includes all covariates, especially for high dimensional data. For this reason, an ATE estimation using an appropriate dimension reduction method and semiparametric model for high dimensional data is attracting attention. Semiparametric method or sparse sufficient dimensionality reduction method can be uesd for dimension reduction for the estimation of propensity score and outcome model. Recently, another method has been proposed that does not use propensity score and outcome regression. After reducing dimension of covariates, ATE estimation can be performed using matching. Among the studies on ATE estimation methods for high dimensional data, four recently proposed studies will be introduced, and how to interpret the estimated ATE will be discussed.

• Nutrition Research and Practice
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• v.18 no.1
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• pp.132-148
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• 2024

BACKGROUND/OBJECTIVES: This study aimed to assess the current mean daily intake of 10 food groups, analyze the sociodemographic factors associated with food consumption, and determine the associations between food consumption/dietary intake and the prevalence rates of obesity, type 2 diabetes (T2D), and hypertension (HTN) in Jakarta, Indonesia. SUBJECTS/METHODS: A total of 600 participants aged 20-85 yrs were included in this cross-sectional study. Food consumption and dietary habits were assessed using a food frequency questionnaire. To determine the association between food consumption/dietary habits and the abovementioned diseases, logistic regression analysis was performed. RESULTS: The average vegetable and fruit intake was lower, while sugar and salt consumption were higher than that recommended by Indonesia's national dietary guidelines. A high intake of ultra-processed foods (UPFs) was associated with young age, men, "single" status, a high education level, and employment with a high monthly income. Obesity and T2D were positively correlated with high intakes of cereals and tubers, UPFs, sugars, fats, and oils. Conversely, an inverse association was found between legume, vegetable, and fruit consumption and obesity risk. An inverse correlation was also observed between vegetable consumption and T2D risk. Moreover, a high salt intake was inversely correlated with fruit consumption in terms of HTN risk. Non-indulgence in habitual late-night snacking and refrainment from consuming more than one dish at each meal were also negatively related to the prevalence of obesity, T2D, and HTN. Inverse correlations were also observed between the prevalence rates of T2D and HTN and abstaining from adding sugar to beverages. CONCLUSION: Foods high in fat, sugar, and sodium were strongly associated with the risks of obesity, T2D, and HTN. Additionally, poor eating habits were also associated with disease development.

• Journal of the Korean Data and Information Science Society
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• v.23 no.1
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• pp.99-111
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• 2012

The log-density ratio of the conditional densities of the predictors given the response variable provides useful information for variable selection in the logistic regression model. In this paper, we consider the predictors that are needed and how they should be included in the model. If the conditional distributions are skewed, the distributions can be considered as gamma distributions. Under this assumption, linear and log terms are generally included in the model. The log-odds graph is a very useful graphical tool in this study. A graphical study is presented which shows that if the conditional distributions of x|y for the two groups overlap significantly, we need both the linear and quadratic terms. On the contrary, if they are well separated, only the linear or log term is needed in the model.

• The Korean Journal of Applied Statistics
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• v.30 no.5
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• pp.775-791
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• 2017

We frequently encounter binary data in real life. Logistic, Probit, Cauchit, Complementary log-log models are often used for binary data analysis. In order to analyze binary data, Liu (2004) proposed a Robit model, in which the inverse of cdf of the Student's t distribution is used as a link function. Kim et al. (2008) also proposed a generalized t-link model to make the binary regression model more flexible. The more flexible skewed distributions allow more flexible link functions in generalized linear models. In the sense, we propose a binary data regression model using skewed generalized t distributions introduced in Theodossiou (1998). We implement R code of the proposed models using the glm function included in R base and R sgt package. We also analyze Pima Indian data using the proposed model in R.

• Fire Science and Engineering
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• v.28 no.2
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• pp.69-75
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• 2014

This study derives the factors which affect the occurrence of arson from statistical data (population, economic, and social factors) by multiple regression analysis. Multiple regression analysis applies to 4 forms of functions, linear functions, semi-log functions, inverse log functions, and dual log functions. Also analysis respectively functions by using the stepwise progress which considered selection and deletion of the independent variable factors by each steps. In order to solve a problem of multiple regression analysis, autocorrelation and multicollinearity, Variance Inflation Factor (VIF) and the Durbin-Watson coefficient were considered. Through the analysis, the optimal model was determined by adjusted Rsquared which means statistical significance used determination, Adjusted R-squared of linear function is scored 0.935 (93.5%), the highest of the 4 forms of function, and so linear function is the optimal model in this study. Then interpretation to the optimal model is conducted. As a result of the analysis, the factors affecting the arson were resulted in lines, the incidence of crime (0.829), the general divorce rate (0.151), the financial autonomy rate (0.149), and the consumer price index (0.099).

• Safety and Health at Work
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• v.14 no.1
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• pp.93-99
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• 2023

Background: Effort-reward imbalance (ERI) and overcommitment at work have been associated poorer mental health. However, nonlinear and nonadditive effects have not been investigated previously. Methods: The association between effort, reward, and overcommitment with odds of poorer mental health was examined among a sample of 68 formal United States waste workers (87% male). Traditional, logistic regression and Bayesian Kernel machine regression (BKMR) modeling was conducted. Models controlled for age, education level, race, gender, union status, and physical health status. Results: The traditional, logistic regression found only overcommitment was significantly associated with poorer mental health (IQR increase: OR = 6.7; 95% CI: 1.7 to 25.5) when controlling for effort and reward (or ERI alone). Results from the BKMR showed that a simultaneous IQR increase in higher effort, lower reward, and higher overcommitment was associated with 6.6 (95% CI: 1.7 to 33.4) times significantly higher odds of poorer mental health. An IQR increase in overcommitment was associated with 5.6 (95% CI: 1.6 to 24.9) times significantly higher odds of poorer mental health when controlling for effort and reward. Higher effort and lower reward at work may not always be associated with poorer mental health but rather they may have an inverse, U-shaped relationship with mental health. No interaction between effort, reward, or overcommitment was observed. Conclusion: When taking into the consideration the relationship between effort, reward, and overcommitment, overcommitment may be most indicative of poorer mental health. Organizations should assess their workers' perceptions of overcommitment to target potential areas of improvement to enhance mental health outcomes.

• Korean Journal of Agricultural and Forest Meteorology
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• v.2 no.4
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• pp.175-182
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• 2000

Spatial interpolation of daily temperature forecasts and observations issued by public weather services is frequently required to make them applicable to agricultural activities and modeling tasks. In contrast to the long term averages like monthly normals, terrain effects are not considered in most spatial interpolations for short term temperatures. This may cause erroneous results in mountainous regions where the observation network hardly covers full features of the complicated terrain. We developed a spatial interpolation model for daily minimum temperature which combines inverse distance squared weighting and elevation difference correction. This model uses a time dependent function for 'mountain slope lapse rate', which can be derived from regression analyses of the station observations with respect to the geographical and topographical features of the surroundings including the station elevation. We applied this model to interpolation of daily minimum temperature over the mountainous Korean Peninsula using 63 standard weather station data. For the first step, a primitive temperature surface was interpolated by inverse distance squared weighting of the 63 point data. Next, a virtual elevation surface was reconstructed by spatially interpolating the 63 station elevation data and subtracted from the elevation surface of a digital elevation model with 1 km grid spacing to obtain the elevation difference at each grid cell. Final estimates of daily minimum temperature at all the grid cells were obtained by applying the calculated daily lapse rate to the elevation difference and adjusting the inverse distance weighted estimates. Independent, measured data sets from 267 automated weather station locations were used to calculate the estimation errors on 12 dates, randomly selected one for each month in 1999. Analysis of 3 terms of estimation errors (mean error, mean absolute error, and root mean squared error) indicates a substantial improvement over the inverse distance squared weighting.