Randomized controlled trials (RCTs) are considered the most rigorous study design for testing hypotheses and the gold standard for evaluating intervention effectiveness. However, RCTs are often conducted under the assumption of ideal conditions that may differ from real-world scenarios in which various issues, such as loss to follow-up, mistakes in participant enrollment or intervention, and low subject compliance or adherence, may occur. There are various group-defining strategies for analyzing RCT data, including the intention-to-treat (ITT), as-treated, and per-protocol (PP) approaches. The ITT principle involves analyzing all participants according to their initial group assignments, regardless of study completion and compliance or adherence to treatment protocols. This approach aims to replicate real-world clinical settings in which several anticipated or unexpected conditions may occur with regard to the study protocol. For the PP approach, only participants who meet the inclusion criteria, complete the interventions according to the study protocols, and have primary outcome data available are included. This approach aims to confirm treatment effects under optimal conditions. In general, the ITT principle is preferred for superiority and inequality trials, whereas the PP approach is preferred for equivalence and non-inferiority trials. However, both analytical approaches should be conducted and their results compared to determine whether significant differences exist. Overall, using both the ITT and PP approaches can provide a more complete picture of the treatment effects and ensure the reliability of the trial results.

Tables and figures are commonly adopted methods for presenting specific data or statistical analysis results. Figures can be used to display characteristics and distributions of data, allowing for intuitive understanding through visualization and thus making it easier to interpret the statistical results. To maximize the positive aspects of figure presentation and increase the accuracy of the content, in this article, the authors will describe how to choose an appropriate figure type and the necessary components to include. Additionally, this article includes examples of figures that are commonly used in research and their essential components using virtual data.

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.

Most diseases have more than two interventions or treatment methods, and the application of network meta-analysis (NMA) studies to compare and evaluate the superiority of each intervention or treatment method is increasing. Understanding the concepts and processes of systematic reviews and meta-analyses is essential to understanding NMA. As with systematic reviews and meta-analyses, NMA involves specifying the topic, searching for and selecting all related studies, and extracting data from the selected studies. To evaluate the effects of each treatment, NMA compares and analyzes three or more interventions or treatment methods using both direct and indirect evidence. There is a possibility of several biases when performing NMA. Therefore, key assumptions like similarity, transitivity, and consistency should be satisfied when performing NMA. Among these key assumptions, consistency can be evaluated and quantified by statistical tests. This review aims to introduce the concepts of NMA, analysis methods, and interpretation and presentation of the results of NMA. It also briefly introduces the emerging issues in NMA, including methods for evaluation of consistency.

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.

Most diseases have more than two interventions or treatment methods, and the application of network meta-analysis (NMA) studies to compare and evaluate the superiority of each intervention or treatment method is increasing. Understanding the concepts and processes of systematic reviews and meta-analyses is essential to understanding NMA. As with systematic reviews and meta-analyses, NMA involves specifying the topic, searching for and selecting all related studies, and extracting data from the selected studies. To evaluate the effects of each treatment, NMA compares and analyzes three or more interventions or treatment methods using both direct and indirect evidence. There is a possibility of several biases when performing NMA. Therefore, key assumptions like similarity, transitivity, and consistency should be satisfied when performing NMA. Among these key assumptions, consistency can be evaluated and quantified by statistical tests. This review aims to introduce the concepts of NMA, analysis methods, and interpretation and presentation of the results of NMA. It also briefly introduces the emerging issues in NMA, including methods for evaluation of consistency.

Objective: This study aimed to investigate trends in the rate of cesarean sections (CSs) in South Korea from 2006 to 2015 and identify the risk factors associated with these changes. Methods: Using the National Health Insurance Corporation dataset, all women who gave birth between 2006 and 2015 were included in the study. We investigated 1) the mode of delivery, 2) the complication rates during pregnancy (i.e., preeclampsia and placenta previa), and 3) pre-pregnancy factors (body mass index, hypertension [HTN], diabetes mellitus [DM], and other pre-existing medical conditions), and their trends during the study period. Results: Over 10 years, the rate of CS increased from 36.3% in 2006 to 40.6% in 2015 (P<0.01). The rate of CS increased in primiparous women, women with multiple pregnancy, and women with preeclampsia. Maternal age and the incidence of placenta previa also increased. In contrast, the rate of vacuum deliveries and vaginal birth after CS decreased during the study period. The rate of women with pre-pregnancy obesity and DM increased, but the rate of women with pre-pregnancy HTN decreased. Conclusion The rate of CS in South Korea increased from 2006 to 2015. This trend may reflect changes in the rate of different risk factors. Identifying the causes of the increasing CS trend observed in this study will allow clinicians to monitor these factors and possibly reduce the rate of CS.

Objective: This study aimed to investigate trends in the rate of cesarean sections (CSs) in South Korea from 2006 to 2015 and identify the risk factors associated with these changes. Methods: Using the National Health Insurance Corporation dataset, all women who gave birth between 2006 and 2015 were included in the study. We investigated 1) the mode of delivery, 2) the complication rates during pregnancy (i.e., preeclampsia and placenta previa), and 3) pre-pregnancy factors (body mass index, hypertension [HTN], diabetes mellitus [DM], and other pre-existing medical conditions), and their trends during the study period. Results: Over 10 years, the rate of CS increased from 36.3% in 2006 to 40.6% in 2015 (P<0.01). The rate of CS increased in primiparous women, women with multiple pregnancy, and women with preeclampsia. Maternal age and the incidence of placenta previa also increased. In contrast, the rate of vacuum deliveries and vaginal birth after CS decreased during the study period. The rate of women with pre-pregnancy obesity and DM increased, but the rate of women with pre-pregnancy HTN decreased. Conclusion The rate of CS in South Korea increased from 2006 to 2015. This trend may reflect changes in the rate of different risk factors. Identifying the causes of the increasing CS trend observed in this study will allow clinicians to monitor these factors and possibly reduce the rate of CS.

Among the different providers of health care big data in Korea, the data provided by the National Health Insurance Database include the medical information of all the citizens who have subscribed to medical insurance. As such, the data have representativeness and completeness. In order to conduct research using these National Health Insurance Database data, it is necessary to understand the characteristics of the claim data to avoid various biases, and to control confounding variables when making various operational definitions in the planning stage of the research. Moreover, without a proper understanding of the big data, it is possible during the analysis and data interpretation to mistakenly interpret the correlation between variables as a causal relationship. Therefore, in order to help advanced medical science, which reflects the medical reality such as medical expenses and number of hospital visits by clearly recognizing and analyzing the characteristics and limitations of health care big data, this author has dealt with the use of data sharing services provided by the National Health Insurance Database.

Systematic reviews and meta-analyses present results by combining and analyzing data from different studies conducted on similar research topics. In recent years, systematic reviews and meta-analyses have been actively performed in various fields including anesthesiology. These research methods are powerful tools that can overcome the difficulties in performing large-scale randomized controlled trials. However, the inclusion of studies with any biases or improperly assessed quality of evidence in systematic reviews and meta-analyses could yield misleading results. Therefore, various guidelines have been suggested for conducting systematic reviews and meta-analyses to help standardize them and improve their quality. Nonetheless, accepting the conclusions of many studies without understanding the meta-analysis can be dangerous. Therefore, this article provides an easy introduction to clinicians on performing and understanding meta-analyses.
Anesthesiology ; Bias (Epidemiology)