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 The main reasons lie in outdated conceptual understanding, monotonic implementing form and lack of teaching resources for labor education.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

Academies and Institutes/history* ; Animals ; Biostatistics/history* ; China ; Ecology/history* ; History, 20th Century ; Humans ; Insecta/physiology* ; Vertebrates/physiology* ; Zoology/history*

The transcriptome represents the complete set of RNA transcripts that are produced by the genome under a specific circumstance or in a specific cell. High-throughput methods, including microarray and bulk RNA sequencing, as well as recent advances in biostatistics based on machine learning approaches provides a quick and effective way of identifying novel genes and pathways related to asthma, which is a heterogeneous disease with diverse pathophysiological mechanisms. In this manuscript, we briefly review how to analyze transcriptome data and then provide a summary of recent transcriptome studies focusing on asthma pathogenesis and asthma drug responses. Studies reviewed here are classified into 2 classes based on the tissues utilized: blood and airway cells.
Asthma ; Biostatistics ; Genetics ; Genome ; Machine Learning ; RNA ; Sequence Analysis, RNA ; Transcriptome

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

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

BACKGROUND: This study aims to conduct curriculum analysis on health management schools focusing on relationship with licence and certification in Republic of Korea. METHODS: Possible employment field, licence and certification as well as curriculum were collected from the home page of 30 health management schools. The subjects and credits of curriculum were analyzed using descriptive statistics. Main subjects by areas were drew using categorization and ranking within qualitative methods. Comparative analysis was conducted for checking relationship between main subject and possible employment field, licence and certification. RESULTS: First, major employment fields after graduation were public health officer, general hospital and clinic, and National Health Insurance Service. Possible licence and certificate were hospital administrator, medical recorder, health education specialist, and medical insurance specialist. Second, total graduate credits were 133.9 including 79.0 for major education, 30.5 for of general education, and 30.5 for elective courses. Third, main subjects were reviewed by areas including basic medicine, health management, hospital business & management, medical records & information, insurance billing & assessment, healthcare marketing & tourism, and health education. There were highest number of subjects on health education area among 8 categories. By subjects, many health management schools open health law, medical terminology, introduction to public health, and biostatistics. Relationship between main subjects and possible employment field, licence and certification in health management schools was strong. CONCLUSION: It is necessary to review curriculum and for improving educational quality in health management schools. Also, development of curriculum standards for courses in health administration and introduction of accreditation system can be considered.
Accreditation ; Biostatistics ; Certification ; Commerce ; Curriculum ; Education ; Employment ; Health Care Sector ; Health Education ; Hospital Administrators ; Hospitals, General ; Humans ; Insurance ; Jurisprudence ; Medical Records ; National Health Programs ; Public Health ; Republic of Korea ; Specialization

Pain is subjective, while statistics related to pain research are objective. This review was written to help researchers involved in pain research make statistical decisions. The main issues are related with the level of scales that are often used in pain research, the choice of statistical methods between parametric or nonparametric statistics, and problems which arise from repeated measurements. In the field of pain research, parametric statistics used to be applied in an erroneous way. This is closely related with the scales of data and repeated measurements. The level of scales includes nominal, ordinal, interval, and ratio scales. The level of scales affects the choice of statistics between parametric or non-parametric methods. In the field of pain research, the most frequently used pain assessment scale is the ordinal scale, which would include the visual analogue scale (VAS). There used to be another view, however, which considered the VAS to be an interval or ratio scale, so that the usage of parametric statistics would be accepted practically in some cases. Repeated measurements of the same subjects always complicates statistics. It means that measurements inevitably have correlations between each other, and would preclude the application of one-way ANOVA in which independence between the measurements is necessary. Repeated measures of ANOVA (RMANOVA), however, would permit the comparison between the correlated measurements as long as the condition of sphericity assumption is satisfied. Conclusively, parametric statistical methods should be used only when the assumptions of parametric statistics, such as normality and sphericity, are established.
Analysis of Variance ; Biostatistics ; Normal Distribution ; Pain Measurement ; Visual Analog Scale ; Weights and Measures

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

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

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