No abstract available.
Sample Size*

No abstract available
Sample Size*

No abstract available.
Sample Size*

At first, we review a sample size estimation method for log-rank test in survival analysis. Although it is widely used these days, it has a weakness for practical use. We propose a modification method to avoid the weakness.
Sample Size*

Repeated measures designs are widely used in the field of anesthesiology because they allow the detection of within-person change over time and provide a higher statistical power for detecting differences than a single measure design while reducing the costs and efforts to conduct a study. However, the complex process of calculating the sample size for repeated measures design requires profound statistical knowledge and also programming skills in some instances. In the present article, the author describes 1) the basic statistics for repeated measures design, 2) the explanation for G Power software, and 3) how to calculate the sample size using an example.
Anesthesiology ; Sample Size*

No abstract available.
Analysis of Variance* ; Sample Size*

The purpose of clinical trials is to find evidences for the effects of experimental new drugs or treatments on human. For the successful clinical trials, it is not sufficient to use statistics only for the analyses of collected data, but it is necessary to extend the usage of statistics in various ways. At the beginning of the study, one needs to use statistics for systematically and concretly planning the study. For this, we discussed the usage of statistics in defining the effect, determining the sample size, statistical analyses, and so on.
Humans ; Sample Size

The proper use of statistics in medical studies is very important to draw an objective conclusion. The misuse and misunderstanding of statistics degrade the quality of scientific results. Authors should make an effort to choose proper statistical procedures and report the results appropriately. In this article, we review and summarize aspects of statistical consideration in preparing for publication in medical journals.
Publications ; Sample Size

Sample size computations, which should be done at the planning stage of the study, are necessary for research to estimate a population parameter or test a hypothesis. For causal analysis of observational databases, sample size computations are generally not needed. Post-hoc power analyses, which are typically done with non-significant findings, should not be performed since reporting post-hoc power is nothing more than reporting p values differently. While sample size calculations are typically based on the tradition of significance testing, sample size calculations based on precision are feasible – if not preferred – alternatives. Sample size calculations depend on several factors such as the study objective, scale of measurement of the outcome variable, study design, and sampling design. Computing for sample size is not as straightforward as presented in textbooks but specific strategies may be resorted to in the face of challenges and constraints.
Sample Size ; Power, Psychological

BACKGROUND: Statistical type II error has seemed to be ignored commonly by medical researchers. To control and present a power value could be helpful to reduce this type of error and to improve a quality of scientific decision making. We performed the post-hoc survey of the power of the negative results in Korean Journal of Anesthesiology (KJA). METHODS: One Hundred nineteen articles with negative results published in KJA during a year of 1997 were selected. We collected the numbers of the sample size and calculated the power of the given negative result only when applicable. And each author's attitude to negative results was taken by arbitrary criteria. RESULTS: Median sample size of these negative results was 16 12 (median interquartile range). We can calculate the power only in 43 articles of 119 negative results. Median power is 18.0% (interquartile range 26.0). In thirty six articles (83.8% of 43) the powers are proved to be under 80.0%. And 22 articles (51.2% of 43) have the powers even under 20.0%. We couldn't find any author who included either power or effect size in the article, and there was only one article in which its authors considered their inadequate number of sample size. CONCLUSIONS: We conclude that authors of KJA tend to ignore statistical type II error. In 119 negative results published in KJA during 1997, the calculated powers were very low and were not reported in the text.
Anesthesiology* ; Decision Making ; Sample Size