Spatial analysis and rendering of maps was performed using QGIS, and statistical analyses were performed using Stata/SE software version 13 (StataCorp, College Station, Texas).
Stata se software version 13
Manufactured by StataCorp
Sourced in United States
Stata/SE software version 13 is a statistical software package developed by StataCorp. It provides data management, statistical analysis, and visualization tools for researchers and analysts. Stata/SE is designed to handle large datasets and offers a range of advanced statistical methods.
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Lab products found in correlation
7 protocols using stata se software version 13
1
Spatial Analysis and Statistical Modeling
2
HSV-1 Seroprevalence Predictors and Heterogeneity
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Univariable and multivariable random-effects meta-regression analyses were conducted to identify predictors of HSV-1 seroprevalence (including temporal trend) and sources of between-study heterogeneity. The log-transformed proportions were regressed to estimate risk ratios.
Relevant independent variables were specified a priori: age bracket, age group, assay type (Western blot, ELISA, or other), country’s income, population type, sample size (<100 vs ≥100 subjects), sampling method (probability-based vs non–probability-based sampling), sex, year of data collection, and year of publication. Factors associated with seroprevalence at P ≤ .10 in univariable analysis were included in the final multivariable analysis. Factors associated with seroprevalence at P ≤ .05 in the final multivariable analysis were deemed statistically significant.
For the country’s income variable, countries with available data were grouped according to the World Bank classification [40 ]. For measures that did not include a year of data collection, missing values were imputed using the median of the values calculated by subtracting the year of data collection (when available) from the year of publication. Meta-regression analyses were conducted with Stata/SE software, version 13 [41 ], using the metareg package [42 ].
Relevant independent variables were specified a priori: age bracket, age group, assay type (Western blot, ELISA, or other), country’s income, population type, sample size (<100 vs ≥100 subjects), sampling method (probability-based vs non–probability-based sampling), sex, year of data collection, and year of publication. Factors associated with seroprevalence at P ≤ .10 in univariable analysis were included in the final multivariable analysis. Factors associated with seroprevalence at P ≤ .05 in the final multivariable analysis were deemed statistically significant.
For the country’s income variable, countries with available data were grouped according to the World Bank classification [40 ]. For measures that did not include a year of data collection, missing values were imputed using the median of the values calculated by subtracting the year of data collection (when available) from the year of publication. Meta-regression analyses were conducted with Stata/SE software, version 13 [41 ], using the metareg package [42 ].
3
Evaluating HIV Care Retention Interventions
4
Dietary Patterns Analysis Methodology
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The comparative analyses were made based on the repeated panels model, where each food pattern of each group at each time was estimated considering the total of available and complete participants at the respective times in order to increase the consistency of the patterns obtained.
To evaluate the factors associated with the dietary patterns, the t-Student test and analysis of variance were performed, comparing the means of the scores for each group, divided according to the factors of interest. A significance of 5% was considered for all analyses.
All statistical analyses were performed with the Stata SE software, version 13 (StataCorp LLC, College Station, TX, USA).
To evaluate the factors associated with the dietary patterns, the t-Student test and analysis of variance were performed, comparing the means of the scores for each group, divided according to the factors of interest. A significance of 5% was considered for all analyses.
All statistical analyses were performed with the Stata SE software, version 13 (StataCorp LLC, College Station, TX, USA).
5
Predictors of Severe COVID-19 Disease
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A general description was done estimating the mean and standard deviation or median and interquartile range (IQR) for numerical variables, depending on their distribution. Proportion and its corresponding 95% confidence interval (CI) were used for categorical variables. Continuous variables were analyzed using the t test or Wilcoxon's test. Categorical variables were analyzed using a χ² test with Yates' correction or Fisher's test.
The analysis of results of supplementary tests excluded patients diagnosed with MIS-C. A multiple logistic regression model was developed to identify predictor variables of severe disease. Cases classified as severe and critical according to Dong 13 were regarded as severe, whereas asymptomatic, mild or moderate cases were regarded as not severe. The STATA/SE software, version 13, was used for statistical analysis. The odds ratio (OR) with a 95% CI was used as a measure of association. The final model assessed calibration and discrimination; the former using the Hosmer-Lemeshow test estimated in deciles of risk and the latter using the Receiver Operating Characteristic (ROC) curve. Overall calibration with a p > 0.05 and a discrimination with an area under the ROC curve > 0.7 were considered adequate.
The analysis of results of supplementary tests excluded patients diagnosed with MIS-C. A multiple logistic regression model was developed to identify predictor variables of severe disease. Cases classified as severe and critical according to Dong 13 were regarded as severe, whereas asymptomatic, mild or moderate cases were regarded as not severe. The STATA/SE software, version 13, was used for statistical analysis. The odds ratio (OR) with a 95% CI was used as a measure of association. The final model assessed calibration and discrimination; the former using the Hosmer-Lemeshow test estimated in deciles of risk and the latter using the Receiver Operating Characteristic (ROC) curve. Overall calibration with a p > 0.05 and a discrimination with an area under the ROC curve > 0.7 were considered adequate.
6
Statistical Analysis of Quantitative and Qualitative Data
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Statistical analysis was performed using the STATA/SE software, version 13.1 (College Station, TX, USA: StataCorp LP). Appropriate testing was performed according to the results of normality tests. Student’s t test and the Mann-Whitney test (or Wilcoxon’s test for paired values) were used to analyse quantitative data, and the chi-square or Fisher’s exact test was used to analyse qualitative data. A p value <0.05 was considered statistically significant.
7
Mortality Risk Factors Analysis
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Descriptive statistics were used to summarize the cohort. Associations between categorical variables were analyzed using Pearson’s chi-square test or two-tailed Fisher’s exact test, as appropriate. Logistic regression was used to evaluate risk factors for mortality. Odds ratios with 95% confidence intervals were determined for each risk factor. Factors associated with mortality at the 0.1 significance level in univariate analysis were entered in the full multivariable model using a backward stepwise approach. Survival functions were estimated using the Kaplan-Meier method. STATA/SE software, version 13.1 (StataCorp, College Station, Texas) was used for statistical analysis.
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