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General medical journals such as the Korean Journal of Anesthesiology (KJA) receive numerous manuscripts every year. However, reviewers have noticed that the tables presented in various manuscripts have great diversity in their appearance, resulting in difficulties in the review and publication process. It might be due to the lack of clear written instructions regarding reporting of statistical results for authors. Therefore, the present article aims to briefly outline reporting methods for several table types, which are commonly used to present statistical results. We hope this article will serve as a guideline for reviewers as well as for authors, who wish to submit a manuscript to the KJA.
The survival data and the survival analysis are the data and analysis methods used to study the probability of survival. The survival data consist of a period from the juncture of a start event to the juncture of the end event (occurrence event). The period is called the survival period or survival time. In this way, the method of analysing the survival time of subjects and appropriately summarizing the degree of survival is called survival analysis. To understand and analyse survival analysis methods, researchers must be aware of some concepts. Concepts to be aware of in the survival analysis include events, censored data, survival period, survival function, survival curve and so on. This review focuses on the terms and concepts used in the survival analysis. It will also cover the types of survival data that should be collected and prepared when using actual survival analysis method and how to prepare them.
In this paper, we develop the noninformative priors for the variance ratio in the analysis of covariance model. We use the criterion of the asymptotic matching of coverage probabilities of Bayesian credible intervals with the corresponding frequentist coverage probabilities. We develop the first and the second order matching priors. We reveal that the second order matching prior does not exist in the unbalanced case. However, the second order matching prior can be obtained in the balanced case. It turns out that the reference priors satisfy the first order matching criterion, but Jeffreys' prior does not satisfy even the first order matching criterion. Some simulation study is performed.
According to the central limit theorem, the means of a random sample of size, n, from a population with mean, μ, and variance, σ2, distribute normally with mean, μ, and variance, σ2 n . Using the central limit theorem, a variety of parametric tests have been developed under assumptions about the parameters that determine the population probability distribution. Compared to non-parametric tests, which do not require any assumptions about the population probability distribution, parametric tests produce more accurate and precise estimates with higher statistical powers. However, many medical researchers use parametric tests to present their data without knowledge of the contribution of the central limit theorem to the development of such tests. Thus, this review presents the basic concepts of the central limit theorem and its role in binomial distributions and the Student’s t-test, and provides an example of the sampling distributions of small populations. A proof of the central limit theorem is also described with the mathematical concepts required for its nearcomplete understanding.
Sample size determination is very important part in clinical trials because it in uences the time and the cost of the experimental studies. In this article, we consider the Bayesian methods for sample size determination based on hypothesis testing. Specifically we compare the usual Bayesian method using Bayes factor with the decision theoretic method using Bayesian reference criterion in mean dierence problem for the normal case with known variances. We illustrate two procedures numerically as well as graphically.
We study Bayesian estimates for finite population proportions in multinomial problems. To do this, we consider a three-stage hierarchical Bayesian model. For prior, we use Dirichlet density to model each cell probability in each cluster. Our method does not require complicated computation such as Metropolis-Hastings algorithm to draw samples from each density of parameters. We draw samples using Gibbs sampler with grid method. We apply this algorithm to a couple of simulation data under three scenarios and we estimate the finite population proportions using two kinds of approaches. We compare results with the point estimates of finite population proportions and their standard deviations. Finally, we check the consistency of computation using diffierent samples drawn from distinct iterates. We study Bayesian estimates for finite population proportions in multinomial problems. To do this, we consider a three-stage hierarchical Bayesian model. For prior, we use Dirichlet density to model each cell probability in each cluster. Our method does not require complicated computation such as Metropolis-Hastings algorithm to draw samples from each density of parameters. We draw samples using Gibbs sampler with grid method. We apply this algorithm to a couple of simulation data under three scenarios and we estimate the finite population proportions using two kinds of approaches. We compare results with the point estimates of finite population proportions and their standard deviations. Finally, we check the consistency of computation using diffierent samples drawn from distinct iterates.
Missing values and outliers are frequently encountered while collecting data. The presence of missing values reduces the data available to be analyzed, compromising the statistical power of the study, and eventually the reliability of its results. In addition, it causes a significant bias in the results and degrades the efficiency of the data. Outliers significantly affect the process of estimating statistics (e.g., the average and standard deviation of a sample), resulting in overestimated or underestimated values. Therefore, the results of data analysis are considerably dependent on the ways in which the missing values and outliers are processed. In this regard, this review discusses the types of missing values, ways of identifying outliers, and dealing with the two.
난치성 암환자의 암성통증은 암환자의 삶의 질을 지극히 떨어뜨리는 중요한 요인이다. 전 세계적으로 암성 통증을 줄이기 위한 노력으로 양방과 한방의 통합적 치료방법이 증가하는 추세이다. 국내외 적으로도 통합적 접근을 통한 암성통증과 관련된 연구가 지속적으로 이루어지고 있으며 암 치료에 있어서는 화학요법으로 인한 오심이나 구토를 조절하기 위해 표준화된 항구토제와 병용한 침 치료가 효과적임을 보고한 무작위대조군 임상연구가 있다. 여러 연구에서 암으로 인한 피로, 방사선 치료로 인한 구강 건조증, 그리고 불면, 불안, 삶의 질 등에 침치료가 효과적이라고 보고한 바 있다. 그러나 침치료의 실제적인 임상적 효과와 다양한 증례 보고 에도 불구하고, 침을 이용한 통합치료의 진통 감소효과의 유의성에 대해서는 아직 이견이 많다. 이에 따라 본 연구에서는 전 세계 출판된 논문검색을 통하여 침을 이용한 양 한방 통합치료로 인해 암성통증의 감소효과를 파악하고 각 논문에서 제시하는 값을 종합하여 침을 이용한 통합치료의 효과가 어느 정도인지를 평가하기 보았다. Cancer pain is a very important factor in cancer patients refractory to drop the quality of life of cancer patients. The worldwide trend is an integrated effort by both the western medicine and korean traditional medicine of treatment increases to reduce cancer pain. There are many studies related to cancer pain through an integrated medicine approach. Many study was reported that acupuncture treatment is effective for fatigue, xerostomia, insomnia, anxiety and quality of life. However, despite the practical clinical effects and various case reports of acupuncture, many still disagree about the significance of an integrated treatment of pain reduction with acupuncture. Therefore, we has identified that reduce effect of comprehensive and integrative treatment using acupuncture for cancer pain through publication review. And we evaluated effect of comprehensive and integrative treatment using acupuncture through summary of values in each publication.