Questionnaires were completed on paper, and answers were transferred to Microsoft Excel (Redmond, WA). Statistical analyses were performed using StataCorp 2017 (Stata Statistical Software 15; StataCorp LLC, College Station, TX). chi-square analyses were used to 1) examine infection prevalence between groups of children when stratified by river contact behavior, 2) assess for differences in the percentage of correct responses in questionnaire data between years, and 3) compare MDA attendance among children from the six MADEX SEP-participating schools to children from six nonparticipating SEP-naïve schools. Matched McNemar’s chi-square analyses were performed on paired within-year questionnaire data (comparing pre- versus posteducation KAP responses). Kolmogorov-Smirnov tests were used to test for differences in male and female answers and Spearman’s rank to assess for correlation between age and percentage of correct responses.
Stata 15 statistical software
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Stata 15 is a comprehensive statistical software package designed for data analysis, management, and visualization. It provides a wide range of tools and commands for various statistical techniques, including regression analysis, time series analysis, and survey data analysis.
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Lab products found in correlation
41 protocols using stata 15 statistical software
1
Evaluating MDA Effectiveness in SEP Schools
2
Blood Pressure Categorization and Predictors by Job Families
3
Factors Influencing Text Message Use for Information Acquisition
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Variables were characterized using both absolute frequencies and percentages. The factors influencing the utilization of a specific information source, namely text messages, to acquire information about the primary form of information, were evaluated through a multivariate analysis. The results of the multivariate analyses are displayed as odds ratios (ORs) accompanied by their standard errors (SE) and a 95% confidence interval (CI). A backward stepwise analysis was carried out to identify the variables to be included in the ultimate multiple logistic regression model, guided by the principles of parsimony and biological plausibility. The threshold for statistical significance was set at p < 0.05. All statistical analyses were executed using Stata Statistical Software 15, developed by StataCorp, College Station, TX, USA.
4
Factors Influencing Romantic/Sexual Relationship Changes During COVID-19
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The data were anonymized by assigning a unique identification number to each participant and electronically stored on a secured file safeguarded by a password. Non-parametric variables were described as absolute and relative frequencies; parametric variables were described as means with standard deviations.
Determinants of the worsening of romantic/sexual relationships following the COVID-19 pandemic were assessed by multivariate analysis. In addition, a backward stepwise logistic-regression analysis was run to define the variables to be included in the final multiple logistic regression model, according to the principles of parsimony and biological plausibility. The results of the multivariate analyses were presented as odds ratio (OR) with standard error (SE) and 95% Confidence Intervals (95% CI). The data were collected using Microsoft Excel (Microsoft Corporation). All analyses were carried out using Stata Statistical Software 15 (StataCorp, College Station, TX, USA).
Determinants of the worsening of romantic/sexual relationships following the COVID-19 pandemic were assessed by multivariate analysis. In addition, a backward stepwise logistic-regression analysis was run to define the variables to be included in the final multiple logistic regression model, according to the principles of parsimony and biological plausibility. The results of the multivariate analyses were presented as odds ratio (OR) with standard error (SE) and 95% Confidence Intervals (95% CI). The data were collected using Microsoft Excel (Microsoft Corporation). All analyses were carried out using Stata Statistical Software 15 (StataCorp, College Station, TX, USA).
5
Determinants of Venous Thromboembolism
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Variables were described as absolute frequencies and percentages. Determinants of VH were assessed by uni- and multivariate analyses. Results of multivariate analyses are presented as an odds ratio (OR) with standard error (SE) and a 95% confidence interval (95% CI). A backward stepwise analysis was run to define the variables to be included in the final multiple logistic regression model, according to the results of univariate models and to the principles of parsimony and biological plausibility. Statistical significance was set at p < 0.05. Data were collected using Microsoft Excel (Microsoft Corporation). All analyses were carried out using Stata Statistical Software 15 (StataCorp, College Station, TX, USA).
6
Adherence to Nicotine Replacement Therapy
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To see the sample size calculation, please refer to the study protocol [25 (link)]. Data were analyzed using STATA Statistical Software 15 (Stata Corp, 2017). Descriptive statistics of demographic information and other key variables were performed. Correlations of key study variables were calculated. Pearson’s correlation coefficients were run when both variables were continuous measures. Otherwise, Spearman’s rank coefficients were run. Due to the non-normal distribution of weeks of NRT use, a Poisson regression analysis was performed. We selected sociodemographic factors (gender, age, and education) as covariates that have been found to be significant correlates of NRT adherence. Incident rate ratios (IRRs) with 95% confidence intervals (CIs) were estimated for predictors. The proportion of missing data was around 3.5%, and the average value of the responses was used to handle missing data if the missingness was less than 25% of the total responses. Cessation outcome was assessed at the eighth counseling session using an intention-to-treat analysis in which all participants assigned to the treatment group were included. A p-value ≤.05 was considered statistically significant.
7
Epidemiological Assessment of Schistosoma mansoni
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All results were recorded in the field with paper and uploaded into Microsoft Excel (Redmond, WA) at a later date. Statistical analyses were performed using StataCorp 2017 (Stata Statistical Software 15, StataCorp LLC, College Station, TX). Chi-squared tests were used to assess the crude relationships between the prevalence by KK and CCA with age, year, gender, and location.
Mixed-effects generalized linear models were used to assess the changes in the prevalence of S. mansoni by both KK and CCA. Tests of departure from linear trend were carried out. Spearman’s rank correlation coefficient was used to examine the linear trend between EPG and year. Simple linear regression analysis was used to examine the association of mean EPG by year, adjusted by location, age, and gender. Results were considered significant if P < 0.05.
Retrospective post hoc sample size calculations were carried out to be able to detect differences in prevalence by KK for each village. To detect a difference in the prevalence of 35% from the baseline prevalence of 74%,22 (link) 31 study participants were required for each village. To account for nonresponse and for subgroup analyses, this agreed with our initial target of 50 recruited participants for each village as recommended in WHO guidelines.13
Mixed-effects generalized linear models were used to assess the changes in the prevalence of S. mansoni by both KK and CCA. Tests of departure from linear trend were carried out. Spearman’s rank correlation coefficient was used to examine the linear trend between EPG and year. Simple linear regression analysis was used to examine the association of mean EPG by year, adjusted by location, age, and gender. Results were considered significant if P < 0.05.
Retrospective post hoc sample size calculations were carried out to be able to detect differences in prevalence by KK for each village. To detect a difference in the prevalence of 35% from the baseline prevalence of 74%,22 (link) 31 study participants were required for each village. To account for nonresponse and for subgroup analyses, this agreed with our initial target of 50 recruited participants for each village as recommended in WHO guidelines.13
8
Evaluation of Vision-Related Quality of Life in SJS/TEN
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The statistical analysis was performed using Stata statistical software 15 (StataCorp, College Station, Texas). Normality of the data were evaluated with the Shapiro-Wilk test. Quantitative variables were expressed as mean ± standard deviation (SD), and qualitative variables were expressed as percentages. Visual acuities were measured with a standardized Snellen chart and converted to logMAR values for analysis. Comparison of NEI-VFQ-25 subscale scores with the subscale scores of the reference population was performed with an unpaired t-test. The Spearman correlation coefficient was used to test the associations between the NEI-VFQ-25 subscale scores and OSDI scores, between the NEI-VFQ-25 composite scores and OSDI scores, and between the NEI-VFQ-25 composite scores and patient-related parameters such as patient age, BCVA in the worse eye, and the duration of time since the acute phase of SJS/TEN. A two-sided p value of <0.05 was considered statistically significant.
9
Determinants of Vaccine Hesitancy Assessed
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All variables were reported as absolute and relative frequencies. Determinants of VH were assessed by uni- and multivariate analyses. A backward stepwise analysis with a significance level of entry and removal equal to 0.2 was run to define the variables to be included in the final multiple logistic regression model, according to the results of univariate models and to the principles of parsimony and biological plausibility. Results of multivariate analyses were reported as odds ratio (OR) and 95% Confidence Interval (CI) (95% CI). χ2 was adopted to compare the incidence of AEFIs between first-dose and second-dose.16 All tests were performed in a two-sided manner, using a nominal significance threshold of P < .05. All statistical analyses were performed using Stata Statistical Software 15 (StataCorp, College Station, TX).
10
Multimorbidity's Impact on Healthcare Utilization
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Regression modelling examined association between multimorbidity and PHC and hospital usage, health expenditures and mortality. Models were adjusted for sex, age, race/ethnicity, educational group, Bolsa Família receipt, and private insurance. Logistic regression was employed for death (binary outcome), Poisson models for counts (PHC consultations and hospital admissions), and linear regression for household health expenditures (subset of population with available data). AOR were reported for logistic regressions and adjusted rate ratios (ARR) reported for Poisson regression models.
The models were expanded with interactions to test whether the associations between multimorbidity and healthcare use, death and expenditures were different across race/ethnicity, educational attainment, and Bolsa Família status groups (i.e. three interactions per outcome). Post-regression probabilities (of death), rates (of PHC consultation and hospital admissions), and average household expenditures were predicted for the three socioeconomic groups and by multimorbidity status. These are interpreted relative to the five-year study period (i.e. five-year probability of death).
All analyses used robust standard errors and carried out in STATA® Statistical Software 15 (StataCorp LLC).
The models were expanded with interactions to test whether the associations between multimorbidity and healthcare use, death and expenditures were different across race/ethnicity, educational attainment, and Bolsa Família status groups (i.e. three interactions per outcome). Post-regression probabilities (of death), rates (of PHC consultation and hospital admissions), and average household expenditures were predicted for the three socioeconomic groups and by multimorbidity status. These are interpreted relative to the five-year study period (i.e. five-year probability of death).
All analyses used robust standard errors and carried out in STATA® Statistical Software 15 (StataCorp LLC).
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