**Introduction**

*Biochemia Medica*are committed to continuously improve the quality of the articles published in the Journal. This may be achieved by helping authors to improve their manuscripts through peer-review process. One of the major problems in manuscripts submitted to

*Biochemia Medica*is the quality of the data analysis and data presentation. The improper use of statistical methods is unethical because it leads to biased results and incorrect conclusions. Moreover, this is a substantial waste of time and money. The most common errors occurring in

*Biochemia Medica*have already been reported in this Journal (1).

*Biochemia Medica*. It should however be made clear that this article by no means provides a substitute for a comprehensive textbook in biostatistics. On contrary, readers are encouraged to take this only as a reminder and to consult a textbook for a more comprehensive coverage of the issues mentioned in this article.

**Are key results included in the ***Abstract*?

*Abstract*?

*Abstract*. Authors quite often forget that

*Abstract*is sometimes the first (and only) part of the article read by the readers. As stated in the

*Instructions to authors*,

*Abstract*for all original articles must be structured into following four headings:

*Introduction*,

*Materials and methods*,

*Results*and

*Conclusions*. Furthermore,

*Abstract*must be comprehensive and provide key results of the study. If not done so already in the

*Materials and methods*section of the

*Abstract*, authors certainly need to make sure that readers are informed about the number and size of the studied groups. All estimates need to be presented with the appropriate summary measures, confidence intervals and P values (where applicable). For all tested differences and associations, the level of significance must be provided.

**Below is the example for poorly written**

*Results*section of the*Abstract*:Results: The concentration of New BioMarker™ in patients with acute myocardial infarction was higher than in healthy controls (P < 0.05). There was a significant correlation of New BioMarker™ with serum copeptine concentrations.

**The following is the example for well written Results section of the Abstract:**

Results: There were 250 patients with acute myocardial infarction and 232 healthy controls. The concentration of New BioMarker™ was higher in patients than in healthy controls (7.3 ± 0.6 mmol/L

*vs*. 5.4 ± 0.5 mmol/L, respectively; P = 0.002). New BioMarker™ was associated with serum copeptine concentration (r = 0.67, P = 0.026).

**Is ***Statistical analysis* section written well, accurate and comprehensive?

*Statistical analysis*section written well, accurate and comprehensive?

*Statistical analysis*as the subheading within the section

*Materials and methods*. Within the subheading

*Statistical analysis*authors need to explain which statistical tests were used in their data analysis and the rationale for using those tests. Care must be taken to assure that: a) all tests used are listed in the

*Materials and methods*under

*Statistical analysis*, as well as b) that all tests listed are indeed applied in the study. From this section, every reader should be able to understand which test exactly was used for every comparison of the data presented with the

*Results*section. At the end of the

*Statistical analysis*, authors need to state the level of significance applied in their study and statistical program used.

*Statistical analysis*, authors need to make sure to address all issues listed below:

- What kind of data did they have (categorical or numerical)?
- How did they describe their data?
- Did they test their distributions for normality? The name of the normality test needs to be stated.
- How was statistical test chosen to test the possible differences and associations between their data?
- Which statistical test was used for analyzing their categorical data?
- Were the groups large enough to detect the expected effect?
- What was the level of significance in their analysis?
- Which statistical software did they use? The version of the software and complete information on the manufacturer of the statistical software must be provided.

**The following is the example for poorly written**

*Statistical analysis*subheading of the*Materials and methods*section:Statistical analysis

Data were presented as mean ± standard deviation. Differences were tested by t-test. Pearson correlation was used to analyze the association between all studied parameters. Data analysis was done using MedCalc.

**The following is the example for well written**

*Statistical analysis*subheading of the*Materials and methods*section:Statistical analysis

The Kolmogorov-Smirnov test was used to assess the normality of distribution of investigated parameters. All parameters in our study were distributed normally. Data were expressed as mean ± standard deviation. Differences were tested by two-tailed t-test. Pearson’s correlation was used to analyze the association between all studied parameters. The values P < 0.05 were considered statistically significant. Statistical analysis was done using MedCalc 12.1.4.0 statistical software (MedCalc Software, Mariakerke, Belgium).

**Key points to keep in mind when writing the ***Results* section

*Results*section

*Result*section.

- Their descriptive analysis is appropriate;
- They have presented their results with adequate precision and accurately;
- They have provided the measure of confidence for all estimates, if necessary and applicable;
- They have used correct statistical tests for their analysis;
- Their graphs and tables are informative;
- They have provided P value for all tests done in their work;
- They are not making any conclusions on the causal relationship unless their study is an experiment or a clinical trial.

*Results*section of their manuscripts.

**Is the descriptive analysis adequate?**

*Lessons in biostatistics*(7) and elsewhere (8).

**Are results presented with adequate precision and accurately?**

- Age is usually expressed with years and only one decimal is allowed, if absolutely necessary. Only when children are studied, it makes sense to provide age in months and even days. Moreover, age is usually reported as median and range (min-max). So, instead of stating that average age was 55.905 ± 2.112 years, it needs to be stated that the average age was 56 (51-60) years.
- The mean and measure of dispersion (standard deviation) for all laboratory parameters needs to be presented with as many decimals as the results are usually reported on the laboratory test report. It is therefore improper to present the WBC data with three decimals, since this parameter is usually measured and reported with only one decimal. So, instead of stating that WBC number in group A was 13.177 (6.837-15.272) x 10
^{9}/L, authors should report that WBC was 13.2 (6.8-15.3) x 10^{9}/L. - Finally, due to the small number of subjects in both groups, the ratio of females in both groups needs to be provided as the number of the observations divided with the total number of subjects within the group (6/11 and 8/14 instead of 54.5% and 57.1%).
- General rules when reporting frequencies are listed below:
- Percentages are not recommended if the number of subjects in the group is < 100. Instead, ratios should be used (for example, 0.67 instead of 67%).
- Percentages should be presented as whole numbers, without decimals. The exception are percentages < 10%, where one decimal place is allowed, only if necessary and applicable (for example, if percentage is 0.3%).
- For small samples (N < 30), the use of percentages and ratios is not recommended. When their sample size is small, authors are advised to present their data with the number of the observations divided with the total number of subjects within the group (for example, 3/11, instead of 27%).

*Table 1a. The example for erroneously presented results for observations in two groups (groups A and B).*

*Table 1b. The example for correctly presented results for observations in two groups (groups A and B).*

*Table 2a. Examples for flawed presentation of results.*

*Table 2b. Examples for correct presentation of results.*

*Lessons in biostatistics*(9).

**Example:**

Let us say that we wish to compare the AUC for parameter A and B. Their AUC and corresponding 95% confidence intervals are 0.78 (0.63-0.89) and 0.99 (0.80-0.99). The question is: is there a statistically significant difference in the AUC for parameters A and B? Since their 95% confidence intervals overlap (from 0.80 to 0.89) we may conclude that there is no statistically significant difference in those two parameters, for the significance level alpha = 0.05.

It is noteworthy to mention that AUC is always reported with two decimal places, as well as its upper and lower 95% confidence interval limits.

**Were correct statistical tests used for the analysis?**

- Are data normally distributed?
- Are data numerical or categorical?
- How many groups do authors have?
- How big are the studied groups?
- Are the measurements independent?

*Biochemia Medica*:

- Normality is not tested and statistical test is used without the knowledge of the data distribution, or regardless to the sample size.
- Paired statistical test is not used, although dependent observations (for example, repeated measurements) are tested.
- Chi-square test is used even if total number of observations or the number of expected frequencies in the 2x2 table is low.
- Pearson’s coefficient of correlation is calculated even if one variable is measured using the ordinal scale or data distribution significantly deviates from normal distribution.
- Differences between three or more groups are tested with t-test, instead of tests like ANOVA or Kruskal-Walis test.

*post-hoc*comparisons.

*post hoc*test for multiple comparisons.

*Post hoc*test is not done if P > 0.05, when applying tests for testing differences between three or more groups.

*post-hoc*comparisons, because different tests have different uses, as well as advantages and disadvantages (10).

*Statistical analysis*and

*Results*. More comprehensive review on the choice of the right statistical test has been extensively elaborated in this Journal within the section

*Lessons in biostatistics*(11).

**Is P value provided for all tests done in the study?**

**Data interpretation**

*a priori*stated level of significance. This means that differences may be interpreted as significant, only if P value is below the stated level of significance. Expressions like ‘

*borderline significant’*are strongly discouraged and will not be accepted.

- We have observed the difference between our study groups, although not statistically significant.
- Though not statistically significant, concentration of glucose was higher in females than in males.
- There was a trend towards higher values of marker X with increasing concentrations of marker Y. The observed association was unfortunately not statistically significant.

**Correlation analysis**

*et al*. in

*Biochemia Medica*(13).

**Conclusions on the causal relationship**

*i.e.*researcher only observes the differences, associations in variables of interest, without any intervention of the investigator on the study population), it is not acceptable to report any effect or induction of measured parameters. Furthermore, if the study is observational and involves monitoring of some parameters over time, it is justifiable to report the increase and decrease of monitored parameter. Otherwise, expressions like increase and decrease are not acceptable and authors are encouraged to use expressions like higher and lower, instead.

**Incorrect**: Compared with the control group, ox-LDL levels were significantly increased in patients on hemodialysis (P = 0.001).

**Correct**: Compared with the control group, ox-LDL levels were significantly higher in patients on hemodialysis (P = 0.001).

**Incorrect**: We found a significantly decreased level of GPx in blood of asthmatic children as compared to age and sex matched controls (13.61 ± 5.73

*vs.*15.22 ± 6.75, respectively; P = 0.036).

**Correct**: We found a significantly lower level of GPx in blood of asthmatic children as compared to age and sex matched controls (13.61 ± 5.73

*vs.*15.22 ± 6.75, respectively; P = 0.036).

**Incorrect**: We observed that carrying AA genotype is significantly increased in healthy controls compared to patients (OR 2.5, 95% Ci = 1.7-3.9; P = 0.012,).

**Correct**: We observed that frequency of AA genotype is significantly higher in healthy controls compared to patients (OR 2.5, 95% Ci = 1.7-3.9; P=0.012).

**Incorrect**: Obstructive sleep apnea induced the increase in concentrations of hsCRP compared to healthy controls (P = 0.045).

**Correct**: Concentrations of hsCRP were higher in children with obstructive sleep apnea, compared to healthy controls (P=0.045).

**Incorrect**: Logistic regression identified serum copeptin (OR 3.1; 95% Ci = 1.7-12.4; P = 0.043) as an independent predictor of 1-month mortality of patients suffering from traumatic brain injury. We therefore conclude that copeptin induces mortality after traumatic brain injury.

**Correct**: Logistic regression identified serum copeptin (OR 3.1; 95% Ci = 1.7-12.4; P = 0.043) as an independent predictor of 1-month mortality of patients suffering from traumatic brain injury. We therefore conclude that increased serum copeptin concentrations are associated with higher risk of mortality after traumatic brain injury.

**Checklist**

*Biochemia Medica*(Table 3). We strongly encourage authors to check the items from the checklist prior to submitting their work for potential publication in our Journal. The aim of the checklist is to remind authors to some most important issues related to their data analysis and presentation. More extensive checklist for editing and reviewing statistical and epidemiological methodology in biomedical research papers, has already been published in this Journal with the aim to assist statistical reviewers and editors as well as to authors when planning their study and preparing their manuscripts (14).

*Table 3. Checklist for authors who submit their work to Biochemia Medica.*