Statistical analyses used in clinical articles published on the Journal of Korean Neurosurgical Society were identified and appropriateness of statistical aspects in reporting results was assessed. Forty seven clinical articles were selected in this study, which were published from February, 2005 to February, 2006 on the journal. The frequency of statistical analysis was as follows: descriptive statistics only 24 (51.1%), one type of statistical method 10 (21.3%), two or more methods 13 (27.6%). An assessment of statistical aspects was performed in 24 clinical articles reporting inferential statistics. Ten articles (41.7%) did not adequately describe or reference all statistical methods used. There were six articles (25.0%) not reporting the confidence level used as the critical criteria of the statistical significance. In thirteen articles (54.2%) it seems more appropriate to implement multivariate analyses in addition to univariate analyses. We recommend that the journal readers should concentrate on improving their knowledge of basic statistics and statistical review for manuscripts submitted should be sought from professionals in the fields of biostatistics and epidemiology.
Biostatistics ; Epidemiology ; Multivariate Analysis

The current implementation of labor education in college is insufficient and does not match its importance. The main reasons lie in outdated conceptual understanding, monotonic implementing form and lack of teaching resources for labor education. This status quo does not meet the requirements for professional and creative labor works in modern society. In order to address this challenge, we propose to incorporate labor education into professional education. Such incorporation not only mutually promotes both labor and professional education, but also integrates professional knowledge and labor skills during the teaching process, thus combining the elements of traditional labor education with timely requirement for creative labor works. This article introduced a way to incorporate labor education into biostatistics courses, and analyzed the mutually beneficial effect of such approaches.
Biostatistics ; Curriculum ; Humans

The null hypothesis significance test method is popular in biological and medical research. Many researchers have used this method for their research without exact knowledge, though it has both merits and shortcomings. Readers will know its shortcomings, as well as several complementary or alternative methods, as such the estimated effect size and the confidence interval.
Biostatistics ; Confidence Intervals ; Models, Statistical

Most parametric tests start with the basic assumption on the distribution of populations. The conditions required to conduct the t-test include the measured values in ratio scale or interval scale, simple random extraction, normal distribution of data, appropriate sample size, and homogeneity of variance. The normality test is a kind of hypothesis test which has Type I and II errors, similar to the other hypothesis tests. It means that the sample size must influence the power of the normality test and its reliability. It is hard to find an established sample size for satisfying the power of the normality test. In the current article, the relationships between normality, power, and sample size were discussed. As the sample size decreased in the normality test, sufficient power was not guaranteed even with the same significance level. In the independent t-test, the change in power according to sample size and sample size ratio between groups was observed. When the sample size of one group was fixed and that of another group increased, power increased to some extent. However, it was not more efficient than increasing the sample sizes of both groups equally. To ensure the power in the normality test, sufficient sample size is required. The power is maximized when the sample size ratio between two groups is 1 : 1.
Biostatistics ; Normal Distribution ; Sample Size

Clinical trial provide reliable basis for evaluating the efficacy and safty of new treatments. To proceed effectively with clinical trial requires an comprehension of the basic principles of clinical design and biostatistical methods. This review focuses on fundamentals of biostatistical theory, on studies of calculating sample size, on definitions for classsification of evidence in epilepsy trials and on examining biostatistical methods for evaluating efficacy of antiepileptic drugs. This review guide how to understand basic statistical concepts and types of study design for epilepsy trials.
Anticonvulsants ; Biostatistics ; Comprehension ; Epilepsy* ; Sample Size

In statistic tests, the probability distribution of the statistics is important. When samples are drawn from population N (micro, sigma2) with a sample size of n, the distribution of the sample mean X should be a normal distribution N (micro, sigma2/n). Under the null hypothesis micro = micro0, the distribution of statistics z=X-micro0/sigma/radical(n) should be standardized as a normal distribution. When the variance of the population is not known, replacement with the sample variance s2 is possible. In this case, the statistics X-micro0/s/radical(n) follows a t distribution (n-1 degrees of freedom). An independent-group t test can be carried out for a comparison of means between two independent groups, with a paired t test for paired data. As the t test is a parametric test, samples should meet certain preconditions, such as normality, equal variances and independence.
Biostatistics ; Matched-Pair Analysis ; Normal Distribution ; Sample Size

We designed and evaluated an objective structured biostatistics examination (OSBE) on a trial basis to determine whether it was feasible for formative or summative assessment. At Ataturk University, we have a seminar system for curriculum for every cohort of all five years undergraduate education. Each seminar consists of an integrated system for different subjects, every year three to six seminars that meet for six to eight weeks, and at the end of each seminar term we conduct an examination as a formative assessment. In 2010, 201 students took the OSBE, and in 2011, 211 students took the same examination at the end of a seminar that had biostatistics as one module. The examination was conducted in four groups and we examined two groups together. Each group had to complete 5 stations in each row therefore we had two parallel lines with different instructions to be followed, thus we simultaneously examined 10 students in these two parallel lines. The students were invited after the examination to receive feedback from the examiners and provide their reflections. There was a significant (P=0.004) difference between male and female scores in the 2010 students, but no gender difference was found in 2011. The comparison among the parallel lines and among the four groups showed that two groups, A and B, did not show a significant difference (P>0.05) in either class. Nonetheless, among the four groups, there was a significant difference in both 2010 (P=0.001) and 2011 (P=0.001). The inter-rater reliability coefficient was 0.60. Overall, the students were satisfied with the testing method; however, they felt some stress. The overall experience of the OSBE was useful in terms of learning, as well as for assessment.
Biostatistics ; Cohort Studies ; Curriculum ; Female ; Humans ; Learning ; Male ; Pilot Projects

Missing observations are common in medical research and health survey research. Several statistical methods to handle the missing data problem have been proposed. The EM algorithm (Expectation-Maximization algorithm) is one of the ways of efficiently handling the missing data problem based on sufficient statistics. In this paper, we developed statistical models and methods for survey data with multivariate missing observations. Especially, we adopted the Em algorithm to handle the multivariate missing observations. We assume that the multivariate observations follow a multivariate normal distribution, where the mean vector and the covariance matrix are primarily of interest. We applied the proposed statistical method to analyze data from a health survey. The data set we used came from a physician survey on Resource-Based Relative Value Scale(RBRVS). In addition to the EM algorithm, we applied the complete case analysis, which used only completely observed cases, and the available case analysis, which utilizes all available information. The residual and normal probability plots were evaluated to access the assumption of normality. We found that the residual sum of squares from the EM algorithm was smaller than those of the complete-case and the available-case analyses.
Biostatistics ; Dataset ; Health Surveys* ; Models, Statistical ; Relative Value Scales

Blind review is one of the most important milestones in clinical trials, which connects data management process to statistical analysis. During blind review, data quality should be reviewed and assessed on both data management and statistical aspects. The primary work of data managers in blind review is to ensure the accuracy of data before it is handed over to biostatistics group. Database auditing, listing data reviewing and reconciliation should become a good clinical data management practice. Statisticians, on the other hand, will focus on quality findings related to protocol deviations or protocol violations. To investigate the protocol deviations and/or violations and relevant impacts on data outcomes, it is important to provide the essential basis of data quality through the blind review, and to assess the reliability of study outcomes.
Biostatistics ; Clinical Trials as Topic ; Data Accuracy ; Databases, Factual ; Reproducibility of Results

Multicollinearity represents a high degree of linear intercorrelation between explanatory variables in a multiple regression model and leads to incorrect results of regression analyses. Diagnostic tools of multicollinearity include the variance inflation factor (VIF), condition index and condition number, and variance decomposition proportion (VDP). The multicollinearity can be expressed by the coefficient of determination (R(h)²) of a multiple regression model with one explanatory variable (X(h)) as the model’s response variable and the others (X(i) [i≠h] as its explanatory variables. The variance (σ(h)²) of the regression coefficients constituting the final regression model are proportional to the VIF(1/1−R(h)²). Hence, an increase in R(h)² (strong multicollinearity) increases σ(h)². The larger σ(h)² produces unreliable probability values and confidence intervals of the regression coefficients. The square root of the ratio of the maximum eigenvalue to each eigenvalue from the correlation matrix of standardized explanatory variables is referred to as the condition index. The condition number is the maximum condition index. Multicollinearity is present when the VIF is higher than 5 to 10 or the condition indices are higher than 10 to 30. However, they cannot indicate multicollinear explanatory variables. VDPs obtained from the eigenvectors can identify the multicollinear variables by showing the extent of the inflation of σ(h)² according to each condition index. When two or more VDPs, which correspond to a common condition index higher than 10 to 30, are higher than 0.8 to 0.9, their associated explanatory variables are multicollinear. Excluding multicollinear explanatory variables leads to statistically stable multiple regression models.
Bias (Epidemiology) ; Biostatistics ; Data Interpretation, Statistical ; Inflation, Economic