Statistical package of social science

We used Visual Basic 6.0/ASP.net and Oracle 8i to manage the data, with stringent range, consistency, and logical checks. To ensure data quality and accuracy, real time data entry was done using netbook. Data were analyzed separately for the hospital and community births using Statistical Package of Social Sciences (SPSS Inc., Chicago, Illinois, USA, SPSS, Version 19.0 for Windows) and Stata (Stata Corp., College Station, Texas, USA, Intercooled Stata 12.0 Version), P ≤ 0.05 was considered statistically significant for all analyses. Descriptive statistics (frequencies, percentages, means and standard deviation) were calculated. We examined the characteristics of newborns and mothers across the allocated groups on a range of variables to determine the degree of balance achieved by the randomization. We analyzed colonization positivity data by intervention groups and follow-up time. Among those that were positive, we further estimated distribution of colony counts by intervention groups and follow-up time. A paired analysis comparing the baseline positivity/colony count of the child with his 2- and 48-hours post intervention positivity/counts was performed to study any group differences as well as changes over follow-up time within group. The chi square, t- test and OR with 95 % CI were used to estimate statistics and significance.

Randomization was carried out by the computer software “Research Randomizer” (Version 3.0; Urbaniak GC, Plous S, 2011, retrieved on March 23, 2011, from www.randomizer.org/). The values presented in this study are means ± SEM. Statistical analyses were calculated using the Statistical Package of Social Sciences (Version 15.0; SPSS Inc., Chicago, IL, USA). The normality distribution of the data was assessed by graphical examination of the histograms and verified by the Shapiro-Wilk test (P > 0.05). Behavioral measurements were analyzed by area under the curve analysis using analysis of variance (ANOVA) followed by the Fisher protected least significant difference test. Student t-test with Bonferroni correction was used to compare data between two groups. An α error rate of 0.05 was taken as the criterion for significance.

Differences between knowledge scores (total and sub-scores on the two sections of the instrument) were assessed using one-way ANOVA.
Internal consistency of the instrument was measured using Cronbach-α constructs, which evaluate how consistently items within each section of the instrument and overall score assess the knowledge. Cronbach-α values range from 0 to 1, with this scale indicating the consistency of responses. Pearson’s correlation was used to assess the test-retest reliability of the GeSNK. A score of 0.7 or greater is considered satisfactory both for internal consistency and reliability [13 ,14 ]. Concurrent validity was also computed using Pearson’s correlation between the GeSNK and the two other questionnaires. The construct validity, instead, was evaluated considering significant differences between two groups of experts (in nutrition and sport sciences) and a control group, using one way ANOVA. Significance was set at p = 0.05. Data were analyzed using the Statistical Package of Social Sciences (SPSS, Chicago, IL, USA) for Windows software program (version 17.0).

Categorical variables were expressed as the number and percentage of patients. Continuous data were presented as either means ± standard deviations if the variables were normally distributed or as medians with interquartile ranges (1st−3rd) if they were non-normally distributed. Fisher's exact test or chi-square test was used to compare categorical variables. The unpaired student's t-test, one-way analysis of variance, Mann–Whitney U test, and Kruskal–Wallis H test were used to compare continuous variables between groups. Factors correlated with preoperative severe FTR and postoperative recurrence of FTR were analyzed using binary logistic regression and Cox proportional hazards regression analysis. In univariate analysis, significant factors (p < 0.1) were included in the multivariate analysis, and forward conditional logistic regression was used to identify the risk factors. The validation of the prediction model was presented as receiver operating characteristic (ROC) curves. Kaplan–Meier (KM) survival analysis was performed to investigate the impact of the length of TA on postoperative FTR recurrence. All statistical analyses were performed using statistical package of social sciences (version 26.0; SPSS Inc., Chicago, Illinois, USA) and Prism 8.2.1 (GraphPad Software Inc., CA, USA) software, and a p-value of < 0.05 was considered statistically significant.