Statistical analysis was carried out with Stata 15 for Windows. χ2 tests that compared categorical sociodemographic variables between groups and analysis of variance (ANOVA) was implemented for quantitative sociodemographic measures. Clinical variable comparison between the studied clinical groups (online GD and IGD) was based on ANOVA and adjusted by the participants’ chronological age (in the case of binary clinical variables, logistic regressions were employed adjusted by the same covariate). Due to multiple statistical comparisons, type-I error was controlled with the Finner’s (1993 ) procedure, which is a method included in the familywise error rate stepwise and is considered a more powerful test than the classical Bonferroni correction. The comparison effect size was estimated through Cohen’s d coefficient (moderate effect size was considered for | d | > 0.50 and high for | d | > 0.80).
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19 protocols using stata 15 for windows
1
Comparing Online and Internet Gaming Disorder
2
Experimental Conditions Influence on Outcomes
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Generalized linear mixed models with random intercepts were used to evaluate the differential effects of the experimental conditions on the selected outcomes. A treatment by time interaction term was included in regression models to calculate the marginal means at individual time points, which were used to graph the time course for different outcomes. Treatment by sex and treatment by time by sex interaction terms were included in regression models to examine sex differences between conditions in the 8-hour average and time course, respectively. Associations between epinephrine, norepinephrine, and BP variables were assessed using Spearman rank correlation coefficients. Outcome variables were adjusted for baseline, age, sex, waist circumference, treatment order, and testing site. Unadjusted data for participant characteristics were compared by independentsamples t test. A probability level of 0.05 was adopted. Statistical analyses were performed blinded to the study conditions using Stata 15 for Windows (StataCorp LP).
3
Impulsivity and Discounting Rates
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Analyses were conducted with Stata15 for Windows. Comparison of discounting rates (k index) and impulsivity levels (UPPS-P) between groups was carried out using analysis of variance (ANOVA, including post-hoc pairwise comparisons through Scheffé's procedure). The effect size for pairwise comparisons in the ANOVA analyses was estimated through the Cohen's-d coefficient (|d|>0.50 was considered moderate effect size and |d|>0.80 was considered large effect size). To avoid increases in type-I error due to multiple comparisons, Finner's procedure was used (a method This article is protected by copyright. All rights reserved.
included in Familywise error rate methods, which offers a more powerful test than Bonferroni correction).
included in Familywise error rate methods, which offers a more powerful test than Bonferroni correction).
4
Predictive Role of Food Addiction on Weight Outcomes
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Statistical analysis was carried out with Stata15 for windows (StataCorp., 2017) . The bivariate comparisons between patients who met and did not meet YFAS 2.0 criteria for FA was based on chi-square tests (χ2) for categorical variables (such as the presence of obesity comorbidities) and T-Test procedures for quantitative measures.
The predictive capacity of the presence of baseline FA on the change in BMI and weight during the dietary intervention was based on mixed 4×2 analysis of variance (ANOVA), define as the intra-subject factor the time of the measurement/assessment (at baseline and at each of the 3 individual counselling sessions), as the between-subject factor the FA group at baseline (absent versus present) and including the patients' sex, age and weight at baseline.
The predictive capacity of the presence of baseline FA on the risk of dropout during the dietary intervention was estimated with logistic regression, while the predictive capacity of the presence of FA prior to the surgery on the likelihood of weight loss at the end of the follow-up was estimated with multinomial regression (this model was employed since group of weight loss includes three categories). Both models, logistic and multinomial regression, were adjusted by the patients' sex, age and the baseline weight.
The predictive capacity of the presence of baseline FA on the change in BMI and weight during the dietary intervention was based on mixed 4×2 analysis of variance (ANOVA), define as the intra-subject factor the time of the measurement/assessment (at baseline and at each of the 3 individual counselling sessions), as the between-subject factor the FA group at baseline (absent versus present) and including the patients' sex, age and weight at baseline.
The predictive capacity of the presence of baseline FA on the risk of dropout during the dietary intervention was estimated with logistic regression, while the predictive capacity of the presence of FA prior to the surgery on the likelihood of weight loss at the end of the follow-up was estimated with multinomial regression (this model was employed since group of weight loss includes three categories). Both models, logistic and multinomial regression, were adjusted by the patients' sex, age and the baseline weight.
5
Gestational Diabetes Prevalence and Complications
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We compared baseline characteristics of the two cohorts using the chi squared test. We computed prevalence of GDM for the two groups and we assessed 95% CI using the exact method [15 (link)]. We compared GDM prevalence and proportions of obstetrical complications using the chi squared test, then computed Odds Ratios (OR) and 95% CI using a univariate logistic regression. We corrected the crude OR by baseline characteristics which had a difference in the two groups of a p-value of 0.2 or less, by multivariate logistic regression, producing adjusted OR (aOR) and 95% CI.
We considered statistically significant a two-tailed p-value inferior to 0.05. We performed all statistical analysis using STATA 15 for Windows.
We considered statistically significant a two-tailed p-value inferior to 0.05. We performed all statistical analysis using STATA 15 for Windows.
6
Sensitivity Analyses of Research Findings
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The following sensitivity analyses were carried out to assess the robustness of our findings:
Statistical analyses were performed with Stata 15 for windows, and a two tailed P<0.05 was considered statistically significant.
Statistical analyses were performed with Stata 15 for windows, and a two tailed P<0.05 was considered statistically significant.
7
Parenting Experiences in Rural and Urban Areas
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Data were described using frequency and percentage for nominal variables, median, and quartiles for monthly income per capita due to its skew distribution, and mean and standard deviation for other numerical variables in the total sample and by rural and urban areas. Chi-square tests, t tests, and Wilcoxon sum rank test were used to compare the differences between rural and urban parents.
In order to further elucidate the direct effect of rural and urban differences on the outcomes of interest by removing the potential indirect effect through the sociodemographic characteristics of the participants, we ran models controlling for the operationalized mediators using general linear for parenting, family adjustment, and parenting efficacy, binary logistic regression for utilization of parenting support services, and multinomial logistic regression for needs of parenting support services. The results of these regression models were presented in the Additional file1 : Appendix.
The significance level was set at 0.05 (two-sided). Data were analyzed with Stata 15 for Windows (StataCorp, TX) and SPSS27 (IBM Corp, NY).
In order to further elucidate the direct effect of rural and urban differences on the outcomes of interest by removing the potential indirect effect through the sociodemographic characteristics of the participants, we ran models controlling for the operationalized mediators using general linear for parenting, family adjustment, and parenting efficacy, binary logistic regression for utilization of parenting support services, and multinomial logistic regression for needs of parenting support services. The results of these regression models were presented in the Additional file
The significance level was set at 0.05 (two-sided). Data were analyzed with Stata 15 for Windows (StataCorp, TX) and SPSS27 (IBM Corp, NY).
8
Personality Profiles in Behavioral Addictions
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Statistical analysis was carried out with Stata 15 for Windows. Pearson's correlation coefficients measured the association between the age of onset and the duration of the problematic addictive behavior with the personality and clinical profile. The specific contribution of the patients' sex, onset, and duration of the problem on the severity of the addiction and the psychopathological state was measured with negative binomial regression and linear multiple regression (for cumulate debts and SCL-90-R GSI score). These models included and tested the interactions sex-by-onset and sex-by-duration: (a) for relevant interaction parameters, single effects for the participants' age were estimated into three groups defined for the quartiles 1 and 3 of the age of onset [early (onset before 20 years of age), medium (onset between 20 and 35 years) and late (onset after 35 years of age)]; and (b) for non-relevant interaction parameters, main effects were estimated and interpreted. Independent models were obtained for each diagnostic subtype (GD, CB, IGD, and SA). Contribution of sex was not explored for IGD and SA, since no women were included in these subsamples due to their low frequency in the groups.
9
Predicting Adrenocortical Carcinoma Using Clinical and CT Features
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Univariate analysis was performed in Statistical Package for the Social Sciences (SPSS) 17.0 for Windows (IBM, Armonk, NY, USA). The distributions of clinical and CT features were compared between patients with and without a diagnosis of ACC, by using the χ2 test (or Fisher’s exact test, when appropriate) for categorical variables and the Mann-Whitney U test for non-normally distributed continuous variables. Subsequently, a multivariable logistic regression model was established in STATA 15 for Windows (StataCorp, College of Station, TX, USA) and nomograms were constructed with the rms package in R version 3.5.0 (R Core Team, 2017). The model discrimination was evaluated by calculating the area under the receiver operating characteristic curve (AUROC). The model calibration was assessed with the Hosmer-Lemeshow goodness-of-fit test and calibration plots. Bootstrapping was used to evaluate the internal validity of the model performance measures. All statistical tests were two-sided, and P<0.05 was considered to be statistically significant.
10
Pathogen Detection in Stool Samples
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Data were analyzed using Stata 15 for Windows (StataCorp LLC, College Station, TX, USA). Pearson's Chi-square test and Fisher's exact test were used to assess correlations between the demographic variables and the type of pathogen detected in the stool. Statistical significance was defined as a p-value of 0.05 for all tests.
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