Appropriate sample size calculation and power analysis have become major issues in research and publication processes. However, the complexity and difficulty of calculating sample size and power require broad statistical knowledge, there is a shortage of personnel with programming skills, and commercial programs are often too expensive to use in practice. The review article aimed to explain the basic concepts of sample size calculation and power analysis; the process of sample estimation; and how to calculate sample size using G*Power software (latest ver. 3.1.9.7; Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Germany) with 5 statistical examples. The null and alternative hypothesis, effect size, power, alpha, type I error, and type II error should be described when calculating the sample size or power. G*Power is recommended for sample size and power calculations for various statistical methods (F, t, χ2, Z, and exact tests), because it is easy to use and free. The process of sample estimation consists of establishing research goals and hypotheses, choosing appropriate statistical tests, choosing one of 5 possible power analysis methods, inputting the required variables for analysis, and selecting the “calculate” button. The G*Power software supports sample size and power calculation for various statistical methods (F, t, χ2, z, and exact tests). This software is helpful for researchers to estimate the sample size and to conduct power analysis.

Even in a well-designed and controlled study, missing data occurs in almost all research. Missing data can reduce the statistical power of a study and can produce biased estimates, leading to invalid conclusions. This manuscript reviews the problems and types of missing data, along with the techniques for handling missing data. The mechanisms by which missing data occurs are illustrated, and the methods for handling the missing data are discussed. The paper concludes with recommendations for the handling of missing data.

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.

To assess the risk factors for clinical anastomotic leakage (AL) in patients undergoing laparoscopic surgery for rectal cancer. Little data are available about risk factors for AL after laparoscopic rectal cancer resection. This was a retrospective analysis of 1609 patients with rectal cancer who had undergone laparoscopic surgery for rectal cancer with sphincter preservation. Clinical data related to AL were collected from 11 institutions. Univariate and multivariate analyses were performed to determine the risk factors for AL. AL was noted in 101 (6.3%) of the patients. The leakage rate ranged from 2.0% to 10.3% for each hospital (P = 0.04). In patients without protective stomas (n = 1187), male sex [hazard ratio (HR), 3.468], advanced tumor stage (HR, 2.520), lower tumor level (HR, 2.418), preoperative chemoradiation (HR, 6.284), perioperative transfusion (HR, 10.705), and multiple firings of the linear stapler (HR, 6.181) were significantly associated with AL. Our theoretical model suggested that the HR for patients with 2 risk factors was significantly higher than that the HR for patients with no or only 1 risk factor. Male sex, low anastomosis, preoperative chemoradiation, advanced tumor stage, perioperative bleeding, and multiple firings of the linear stapler increased the risk of AL after laparoscopic surgery for rectal cancer. A diverting stoma might be mandatory in patients with 2 or more of the risk factors identified in this analysis.

Systematic reviews and meta-analyses rank the highest in the evidence hierarchy. However, they still have the risk of spurious results because they include too few studies and participants. The use of trial sequential analysis (TSA) has increased recently, providing more information on the precision and uncertainty of meta-analysis results. This makes it a powerful tool for clinicians to assess the conclusiveness of meta-analysis. TSA provides monitoring boundaries or futility boundaries, helping clinicians prevent unnecessary trials. The use and interpretation of TSA should be based on an understanding of the principles and assumptions behind TSA, which may provide more accurate, precise, and unbiased information to clinicians, patients, and policymakers. In this article, the history, background, principles, and assumptions behind TSA are described, which would lead to its better understanding, implementation, and interpretation.

Choi GJ, et al. J Lipid Atheroscler. 2023 Jan;12(1):3-11. https://doi.org/10.12997/jla.2023.12.1.3