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Introduction
It has been encouraging to see the growing number of outstanding article submissions and publications in the Korean Journal of Anesthesiology (KJA) over the years. Unfortunately, however, the diversity of result presentation format, alongside the number of manuscripts, has resulted in confusion not only in the review and publication process but also in delivering appropriate information to the readers. Presenting results derived using similar statistical methods in a prescribed tabular format recommended by the journal will not only simplify the review and publication process but also help with readers’ understanding of the published content.
Common statistical tests and tables
Various types of tables are used to clearly present various forms of research results. Even if presented independently, tables must contain the essential elements needed to convey the necessary information. For instance, the title must contain sufficient description of the content, while the body and footnotes of the table must describe in detail the statistical method and results of the analysis.
The following are the examples of typical tabular results commonly submitted to this journal. The data used in the examples were generated randomly, unless otherwise indicated, and do not reflect results of a specific study, i.e., the presented results have no clinical significance.
Common guidelines
Statistic provided in all the tables follow the guidelines on the representation of significant figures and statistics in the Instructions for authors provided by the KJA. There must be no blank spaces in the table and estimate, sample size (n), and the statistical method used must be appropriately included when presenting results using statistical analysis. Quantitative data can be expressed as a representative value and its distribution, such as ‘mean ± standard deviation’ or ‘median (first quartile, third quartile). Qualitative data can be expressed as ‘frequency (percent, %)’, et al. For statistical analyses involving variable transformation that changes the shape of the distribution (i.e., log transformation), statistic reflective of the original value should be used. An inverse statistic may also be expressed if needed. Description of the transformation method and transformed values must accompany variable transformation. For more information on data transformation, refer to Lee's article [2].
Based on the statistic derived from the sample data of the study, the population parameters are estimated. It is recommended to display a confidence interval (i.e., 95% CI) that is an interval estimator along with point estimators such as mean, median, proportion, coefficient, et al. The previously derived estimates are described together with the hypothesis test results. P value must be described in three decimal places, and a test statistic should be presented in detail so that statistical inference can be made. Presenting the effect size, if possible, can aid the interpretation of statistical results.
An explanation of the abbreviations must be included in the footnote even if an explanation is provided in the text so that the table can be interpreted independently. The unit of measure of each variable must be accurately described, and the number of samples should be presented in the title or alongside the variable.
One sample comparison
In one sample comparisons, the data of the experimental sample are compared to a specific reference value. The example in Table 1 is a comparison between the arterial pressures of the experimental sample with the reference value of 60 mmHg. Based on the distribution of experimental data, a parametric or non-parametric method of statistical analysis was applied, along with the difference between the reference value and that of the experimental sample and its 95% CI.
In the case of categorical data, proportions, etc. can be compared. One sample proportion test can be performed to compare the response rate with the reference value, and when the response rate is close to 0% or 100%, an exact binomial test is sometimes performed. The comparison results are described in Table 2 along with the 95% CI of the response rate.
Comparison of two independent samples
Table 3 presents the results of comparing the mean arterial pressure and heart rate after endotracheal intubation when two types of endotracheal intubation devices were used. A parametric or non-parametric method of statistical analysis is applied depending on the distribution of the experimental data. The statistical method used is described in the table along with a representative value suitable for the distribution. To facilitate the interpretation of the results, the difference between the two groups is presented and the corresponding P value is presented to three decimal places.
Comparison of matched pairs
Table 4 presents data from a study measuring mean blood pressure before and after the administration of a drug. The table presents results of administering the drug in a sample of hypertensive patients and a sample of patients with a body mass index over 30 kg/m2. The number of patients in each sample has been presented and the mean or median blood pressure was used as the representative value according to the distribution of measurements. A paired t-test was used to perform a paired comparison using the difference in pre- and post-treatment values for each patient. The statistically estimated differences are presented alongside the its 95% CI. The statistical method and P value is also clearly presented.
Comparison of three or more independent samples
Results from a study on pain control following a Cesarean section are presented in Table 5 [3]. The administered dose of morphine and time taken until the first dose was compared between a control group that received normal saline, a group that received intrathecal morphine and a group that received a quadratus lumborum block. Morphine requirement with a normal distribution was expressed as ‘mean ± standard deviation’, while the time taken until the first dose was expressed as a ‘median (Q1, Q3)’ value as it did not satisfy the normal distribution assumption. An accurate P value, up to 3 decimal places, and the number of samples in each group are presented, while a detailed description of the statistical method including the multiple comparison method for post hoc analysis is provided.
Categorical data comparison
Table 6 presents results of an investigation analyzing the occurrence of successful endotracheal intubation, sore throat 1 hour following tracheal extubation, and post-surgical vocal cord paralysis in two groups treated with an existing versus newly developed endotracheal tube. Results were reported as the frequency of occurrence and relative frequency and the statistical method and P value are clearly presented.
Logistic regression analysis
The dependent variable is a nominal scale. This analysis method is widely used when selecting a meaningful variable among various explanatory variables and results are presented in terms of odds ratio, etc. Parts of results of a study published in the KJA is presented in Table 7 [4]. The study analyzed the risk factors of post-anesthesia emergence agitation in the recovery room. Variables with three or more components were converted into insignificant dummy variables to estimate the odds ratio. The table presents the odds ratio of referenced components and those converted into dummy variables alongside the 95% CI. A detailed description of the statistical methods used to select variables in the logistic regression analysis is also included.
Conclusion
This article examined the principles of presenting the statistical results in clinical studies as a table. We hope to see manuscript submissions with standardized tables reflective of the provided framework. Such standardized format will help facilitate the submission and review process for both authors and reviewers.
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