• Communications for Statistical Applications and Methods
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• v.16 no.6
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• pp.945-957
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• 2009

Repeated measurement data among several treatments with a control is often used in the field of medicine study. In this paper, we suggest a method for comparison of the linear trend of responds followed time among several treatments with a control based on repeated measurement data. First, we estimate slope from each subject and generate samples using the slope estimated previous. And then, we test the difference among treatment with a control by ANOVA F test, Jonckheere-Terpstra test, updated control group procedure using generated samples. Monte Carlo Simulation is adapted to compare the power and experimental significance levels in various configuration.

• Communications for Statistical Applications and Methods
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• v.25 no.4
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• pp.373-383
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• 2018

The spatial scan statistic is a widely used method to detect spatial clusters. The method imposes a large number of scanning windows with pre-defined shapes and varying sizes on the entire study region. The likelihood ratio test statistic comparing inside versus outside each window is then calculated and the window with the maximum value of test statistic becomes the most likely cluster. The results of cluster detection respond sensitively to the shape and the maximum size of scanning windows. The shape of scanning window has been extensively studied; however, there has been relatively little attention on the maximum scanning window size (MSWS) or maximum reported cluster size (MRCS). The Gini coefficient has recently been proposed by Han et al. (International Journal of Health Geographics, 15, 27, 2016) as a powerful tool to determine the optimal value of MRCS for the Poisson-based spatial scan statistic. In this paper, we apply the Gini coefficient to normal-based spatial scan statistics. Through a simulation study, we evaluate the performance of the proposed method. We illustrate the method using a real data example of female colorectal cancer incidence rates in South Korea for the year 2009.

• Safety and Health at Work
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• v.5 no.1
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• pp.13-16
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• 2014

Background: This study assesses influences of baseline psychological risk factors on prevalence of low back pain (LBP) at baseline and follow-up among nurses. Methods: A prospective longitudinal study was performed at two phases, baseline and 1-year follow-up among 246 nurses of university hospitals in Shahroud, Iran. A standardized Cultural and Psychosocial Influences on Disability questionnaire was used for data collection. Logistic regression was performed for analysis. Results: At the baseline of the study, 58.9% of nurses reported back pain in the previous 12 months. Age (p = 0.001), belief that work causes pain (p = 0.022), and somatization tendency (p = 0.002) significantly increased risk of LBP. At 1-year follow-up, prevalence of LBP was 45.7% and expectation of back pain at baseline (p = 0.016) significantly increased risk of LBP in this phase (p < 0.05). Conclusion: Results indicate that risk factors for prevalence of back pain at baseline and 1-year follow-up are different. At baseline, the risk factors are age, belief that work causes pain, and somatization tendency, and at follow-up, expectation of pain is the major risk factor.

• The Korean Journal of Applied Statistics
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• v.22 no.5
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• pp.1033-1046
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• 2009

In new drug development studies or clinical trials, zero-dose control is needed in general to determine the lowest dose level for a new drug which can act with our bodies. When the lowest dose level compared with zero-dose control has significant difference in effect, it is referred as minimum effective dose(MED). We propose, in this paper, parametric sequential test using updated control to identify the minimum effective dose(MED) level. Monte Carlo Simulation is adapted to examine the power and experimental significance levels of the proposed method with other methods.

• The Korean Journal of Applied Statistics
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• v.22 no.5
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• pp.1047-1057
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• 2009

The nonparametric bivariate two-sample test of Bennett (1967) is extended to the multivariate k sample test. This test has been easily modified for a monotone trend among k samples. Often in applications it is important to consider a set of multivariate response variables simultaneously, rather than individually, and also important to consider testing k samples altogether. Different approaches of estimating the null covariance matrices of the test statistics resulted in the same limiting form. The multivariate k sample test is applied to the non-normal data of a randomized trial conducted for a period of four weeks in mental hospitals. The purpose of the trial is to compare the efficacy of three different interventions for a relief of the frequently occurring problems of constipation, caused as a side effect of antipsychotic drugs during hospitalization. The bowel movement status of patient for a week is summarized into a single severity score, and severity scores of four weeks comprise a four-dimensional multivariate variable. It is desirable with this trial data to consider a multivariate testing among k samples.

• The Korean Journal of Applied Statistics
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• v.26 no.6
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• pp.889-902
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• 2013

Several goodness-of-fit test statistics have been proposed for a multinomial logit regression model; however, the properties of the proposed tests were not adequately studied. This paper evaluates three different goodness-of-fit tests using grouping strategies, proposed by Fagerland et al. (2008), Bull (1994), and Pigeon and Heyse (1999). In addition, Pearson (1900)'s method is also examined as a reference. Simulation studies were conducted to evaluate the four methods in terms of null distribution and power. A real data example is presented to illustrate the methods.

• The Korean Journal of Applied Statistics
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• v.26 no.6
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• pp.1009-1018
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• 2013

Regression analysis is useful to understand the relationship of variables; however, we need to test if the slope of each regression lines is the same when comparing several populations. This paper suggests a new parallelism test for several linear regression lines. We use F-test of ANOVA and Kruskal-Wallis (1952) tests after obtaining slope estimator from a sequential slope. In addition, a Monte Carlo simulation study is adapted to compare the power of the proposed methods with those of Park and Kim (2009).

• Journal of the Korean Data and Information Science Society
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• v.23 no.1
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• pp.25-37
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• 2012

Many statistics, such as Cohen's (1960) ${\kappa}$, Scott's (1955) ${\pi}$, and Park and Park's (2007) H have been proposed as measures of agreement to represent inter-rater reliability. This study compared bias, SE, MSE, and CV of the measures of agreement with nominal and ordinal categories in the balanced marginal distributions, and those with nominal categories in the two paradoxical situations. As a result, in all cases, AC1and Hhad smaller SE and CV.

• Journal of the Korean Data and Information Science Society
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• v.21 no.6
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• pp.1243-1251
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• 2010

For clinical trials, it is common to compare the placebo and new drug. The method of calculating a sample size for two independent populations are the t-test that is used for parametric methods, and the Wilcoxon rank-sum test that is used in the non-parametric methods. In this paper, we propose a method that is using Kim's (1994) statistic power based on the linear placement statistic, which was proposed by Orban and Wolfe (1982). We also compare the sample size for the proposed method with that for using Wang et al. (2003)'s sample size formula which is based on Wilcoxon rank-sum test, and with that of t-test for parametric methods.

• Journal of the Korean Data and Information Science Society
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• v.24 no.1
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• pp.95-103
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• 2013

Nonparametric procedure in randomized block design (RBD) was proposed by Friedman (1937) for general alternatives. Also Page (1963) suggested the test for ordered alternatives in RBD. In this paper, we proposed the new nonparametric method in randomized block design using aligned method suggested by Hodges and Lehmann (1962) and the joint placement described in Chung and Kim (2007). Also, Monte Carlo simulation study was adapted to compare the power of the proposed procedure with those of previous procedure.