Stata for Windows (version 12.0) (StataCorp, College Station, TX, USA) was used for data analysis. First, t test was used to compare the difference between the two gender groups on continuous variables and Chi-square tests on categorical variables. To examine the bivariate association between SBI, BMI and psychological symptoms, the ANOVA F test was employed. Second, multivariate ordinary least square (OLS) regression analysis was performed to examine the association between SBI, BMI and psychological symptoms while controlling for other covariates [35 (link)]. For each independent variable that was categorical, one category was used as the reference group and the remaining categories were included in the model as dummy variables. Finally, to account for the within-class clustering effect among students, Huber-White adjustment was employed to obtain robust estimates for standard errors and confidence intervals. All significance tests were two-tailed and a p value of 0.05 or lower would be considered as statistically significant.
Stata version 12 for windows
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STATA version 12 for Windows is a statistical software package designed for data analysis, management, and visualization. It provides a wide range of tools for descriptive statistics, regression analysis, time series analysis, and more. STATA version 12 is compatible with the Windows operating system.
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21 protocols using stata version 12 for windows
1
Examining Psychological Symptoms and BMI
2
Insulin Resistance Risk Factors
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Data were analyzed using Stata for Windows version 12.0 (Lakeway Drive College Station, TX, US). A P value of <0.05 denoted statistical significance. Statistical analysis included chi-square tests for categorical variables and Student's t-test for comparison of mean values of anthropometric and cardiometabolic variables. ROC analysis was used to find the optimal cutoff of HOMA-IR for diagnosis of IR. Bivariate analyses were used to test the association between obesity, sarcopenia, unhealthy food intake, physical inactivity, and FHDM and the outcome, IR. We used multiple logistic regressions to assess the influence of the variables significantly associated with IR. We excluded data on participants who reported unknown information in at least one parent or grandparent (n = 125). Four models were estimated. The first one included FHDM and low PA as independent variables. In the second model, obesity was added. The third model included sarcopenia. Finally, a full adjusted model contained all mentioned covariates with the addition of low adiponectin. The likelihood ratio test was performed to test the overall significance of all coefficients in the multiple adjusted models, whereas the Hosmer-Lemeshow goodness-of-fit test was used to evaluate their effectiveness in predicting the outcome of IR.
3
Cardiometabolic Risk Factors in Adolescents
4
Exploring Psychological Distress Changes
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Firstly, descriptive statistics were reported as percentages and frequencies for categorical variables. Then cross-tabulations with chi-square tests were used to examine the distribution of psychological distress changes across subgroups with different family and education backgrounds. Finally, multivariable regression was used to explore the association between students’ psychological distress changes over time and family background and education background, controlling for demographic characteristics and initial level of psychological distress throughout all analyses. Since our outcome variable was categorical and ordinal, we employed the logit-family models. Odds ratios and 95% confidence intervals were reported. More details are offered where necessary in the results section. Finally, since the respondents were clustered within classes, the significance tests were based on robust standard errors, allowing for correlated residuals within classes [48 (link)]. All analyses were performed using Stata for Windows, version 12.0 (StataCorp, College Station, TX, USA).
5
Feasibility and Compliance in Symptom Monitoring
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Feasibility was measured by means of return proportions of symptom diaries and bio samples. Reliability of the parental statements regarding visits to a pediatrician was tested against documentation at the doctors’ offices using Cohen’s kappa and classified according to the scale suggested by Landis and Koch (1977) [37 (link)]. A crude prevalence of symptoms was calculated as the number of days with symptoms in relation to the total number of days. Descriptive analyses were performed to analyze the acceptance of study tasks based on responses in the final questionnaire. We used multivariable logistic regression to detect predictive factors for compliance with the study protocol. Good compliance was defined as ≥75 days of the symptom diary filled and ≥3 nasal swabs and stool samples obtained. Variables that might influence compliance were chosen according to the literature (socioeconomic status indicated by education, age of mother at birth, age of child at study entry, child’s age at nursery school entry) and by a priori assumptions (number of siblings, behavior of child at first nasal swab). A backward elimination process (using p < 0.1 as the decision rule) was performed using the multivariable fractional polynomial approach proposed by Royston and Sauerbrei [38 (link)].
All analyses were performed with Stata for Windows, version 12 (StataCorp, College Station, TX).
All analyses were performed with Stata for Windows, version 12 (StataCorp, College Station, TX).
6
Trends in Blood Donor Infections
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Data was cleaned, recoded and analyzed using Stata for Windows version 12. Summary statistics from the number of individual blood donors in the MBTS database per year for each of this 5 year period were calculated. Annual prevalence was calculated and expressed in percentages per year for each infection for all blood donors and separately for first time blood donors and for repeat blood donors. These were used to plot graphs to illustrate the 5 year trend of annual prevalence. The Cochran-Armitage Test for trend of proportions was used to test the statistical significance of the observed trends.
To determine the relationship between a positive TTI result with age, sex, marital status, occupation and number of donations, univariable and multivariable logistic regression analyses were performed using the latest (2015) group of blood donors. to evaluate the relationship.. Although all blood donors who donated blood in 2015 were used in the analysis, they were still a sample of all blood donors. As such 95% confidence intervals (CI) were calculated for the odds ratios (OR) and the adjusted odds ratios (AOR). For all statistical tests, the level of significance was evaluated at 0.05.
To determine the relationship between a positive TTI result with age, sex, marital status, occupation and number of donations, univariable and multivariable logistic regression analyses were performed using the latest (2015) group of blood donors. to evaluate the relationship.. Although all blood donors who donated blood in 2015 were used in the analysis, they were still a sample of all blood donors. As such 95% confidence intervals (CI) were calculated for the odds ratios (OR) and the adjusted odds ratios (AOR). For all statistical tests, the level of significance was evaluated at 0.05.
7
Myeloproliferative Neoplasm Biomarker Analysis
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Continuous values were expressed as medians and ranges, and nonparametric K-sample test on the equality of medians was used to test the null hypothesis that the K samples were drawn from populations with the same median. Categorical data were given as counts and percentages; chi-square or Fisher exact test was used to test independence between groups, as appropriate. Boxplots were used to graph distributions across mutational groups. Statistical difference in means of hs-CRP and PTX3 values among mutations was tested considering JAK2V617F homozygous category as reference. Bonferroni correction was used to adjust the probability (p) values because of the increased risk of the type I error when making multiple statistical tests. Incidence rate of outcomes of interest (major thrombosis, bleeding, haematological evolution and death) were expressed as % patients/year. Logistic regression models were applied to estimate risk prediction of inflammatory biomarkers on outcomes. Multivariable models were evaluated unadjusted and sequentially adjusted for potential confounding factors. All tests for statistical significance were two-tailed and a value of p < 0.05 was chosen as the cut-off level for statistical significance. The statistical package STATA for Windows version 12 was used for analysis.
8
Age-Specific Sickness Absence Analysis
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Data were analyzed using Stata for Windows, version 12. We performed age-specific comparative descriptive analyses of patterns of sickness absence and sick leave episodes measured by rates and frequencies, with corresponding P values for linear trend. Linear and logistic regression models were applied for the trend tests, respectively.
9
Infant Vocalizations and Demographic Factors
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Analysis was done with Stata for Windows (version 12). Multiple regression was used to compute partial regression coefficients and logistic regression to estimate ORs and Wald 95% CIs (Cohen et al., 2003 ). Age (8–13 weeks vs. older than 13 weeks) and gender were treated as binary variables and included in the model as factors, and dyads as potential confounders. A general linear model (GLM) was used to analyze number of vocalizations per minute, with infants’ gender and age as factors and including partial eta square as index of effect size. A chi square test was applied on the contingency tables, including Cramer’s V as an index of effect size. An alpha level of 0.05 was used for all statistical tests.
10
Comparative Analysis of ARI Definitions
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We applied the different definitions of ARI in the dataset with daily symptom diary collection and determined for each definition the number and duration of ARI episodes for each study participant. We report for each definition overall parameters of disease burden in our dataset (total number of ARI episodes, total days with ARI, symptom-free days included in ARI episodes, mean duration of an ARI episode, median duration of an ARI episode, the minimum and maximum number of episodes per participant, and the minimum and maximum total days with ARI per participant).
In order to assess differences across disease definitions on these parameters of disease burden, we used Friedman tests. 18 All analyses were performed with Stata for Windows, version 12 (StataCorp, College Station, TX).
In order to assess differences across disease definitions on these parameters of disease burden, we used Friedman tests. 18 All analyses were performed with Stata for Windows, version 12 (StataCorp, College Station, TX).
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