The study was designed as a cluster-randomized controlled trial, with extensive monitoring of the health behaviour of households before and after a rapid diagnostic test was made available in local drug shops. Since the study was designed to explore both cross-sectional and pre-post differences, 67 (85%) of the study villages were randomly selected to receive the intervention while the remaining 12 (15%) were used as a control group. The study was implemented between March 2011 and April 2012. Training in the use of the diagnostic test occurred between 21 June and 6 July, 2011. A simple random number draw – generated by Stata/SE version 11.0 (StataCorp. LP, College Station, United States of America) – was used for the selection of study villages and households and the assignment of villages to the intervention or control arm.
Stata se version 11
Manufactured by StataCorp
Sourced in United States
Stata/SE version 11 is a statistical software package designed for data analysis, data management, and graphics. It provides a wide range of tools for researchers, analysts, and professionals working with complex data. The software offers advanced statistical techniques, data visualization capabilities, and a user-friendly interface to facilitate efficient data-driven decision-making.
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Lab products found in correlation
48 protocols using stata se version 11
1
Rapid Diagnostic Test Cluster Trial
2
Anemia and Folate Status in Women
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Analyses were performed using STATA/SE version 11.0 (STATACorp, College Station, Texas) and Excel (Microsoft Corporation, Redmond, Washington). Hemoglobin concentrations were presented as means (standard deviation, SD). Because folate concentration was not normally distributed, the values were log-transformed and expressed as geometric means (95% confidence interval [CI]).
Comparisons of mean values and prevalences (folate and anemia) between women groups (pregnant, breastfeeding, nonpregnant/nonbreastfeeding) and their living areas were made by analysis of variance and Pearson w 2 test. Multivariate regression (stepwise) models were initially performed in order to identify the factors that were associated with the hemoglobin concentration in women. The assumptions of linearity, multicollinearity, homoscedasticity, and normality were examined to ensure that no cases exerted an undue influence on the final multiple regression model. Then, a multivariate logistic regression was performed to examine the association between anemia status (presence/absence) and these potential predictors. Significant odds ratios of predictors were used to predict anemia. The level of statistical significance was set at P < .05 for all analyses.
Comparisons of mean values and prevalences (folate and anemia) between women groups (pregnant, breastfeeding, nonpregnant/nonbreastfeeding) and their living areas were made by analysis of variance and Pearson w 2 test. Multivariate regression (stepwise) models were initially performed in order to identify the factors that were associated with the hemoglobin concentration in women. The assumptions of linearity, multicollinearity, homoscedasticity, and normality were examined to ensure that no cases exerted an undue influence on the final multiple regression model. Then, a multivariate logistic regression was performed to examine the association between anemia status (presence/absence) and these potential predictors. Significant odds ratios of predictors were used to predict anemia. The level of statistical significance was set at P < .05 for all analyses.
3
Longitudinal Medication Use Patterns
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Baseline characteristics of the study sample are described using means and standard deviation (SD) or numbers and proportions where appropriate. We measured the number and percentage (compared to baseline) of subjects starting and stopping medications within the most prevalent therapeutic classes. In an effort to avoid difficult-to-interpret and unwieldy comparisons and descriptions, we focused our analysis on describing trends over time for only the most common therapeutic classes, defined as Anatomical Therapeutic Chemical classification system level 1 groups28 with >20% prevalence at baseline. Analyses were further stratified by age-group (65–74, 75–84, and 85 years and older). We used generalized estimating equations to account for repeated measures and the longitudinal structure of our data. We assumed an exchangeable correlation structure. We specifically tested for changes in medication use over time using an ordinal dummy variable for each period (baseline as reference period) as our independent variable. We considered a 10% absolute change between time periods to indicate a clinically meaningful difference in drug use over time. All analyses were conducted using Stata/SE version 11.0 (StataCorp LP, College Station, TX, USA).
4
Maternal Depression and Child Stunting
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Data analysis was performed using Stata/SE version 11.0 (Stata Corp, College Station, TX, USA). Means and standard deviations were computed for continuous variables and frequencies and percentages for categorical variables. Bivariate analyses were conducted in which maternal depression status (Depressed or Not depressed) was compared with the maternal socio-demographic variables (maternal age, marital status, education level, religion, ethnicity, household wealth tertile) and child characteristics (birthweight, sex, and age) using Chi-square (χ2) for proportions and t-test for means. Maternal socio-demographic characteristics (age, marital status and household wealth tertile) and child characteristic (birth weight) statistically significant in the bivariate analyses were controlled for in a binary logistic regression model that assessed the association between stunting (dependent variable) and maternal depression (independent variable). In all analyses, the level of statistical significance was set at 0.05.
5
Demographic Factors Influence on Role Models
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The data were analysed using Stata/SE version 11.0 (Stata Corp, Texas, USA), to extract the principal attributes of perceived PRMs and NRMs. PRMs and NRMs were compared according to their demographic factors using univariate, One-way ANOVA, independent sample t-test, or Pearson correlation analysis. Hierarchical Clustering/dendrogram was used to measure the similarities among personal attributes. Multivariate linear regression was used to reduce the effect of potential confounders and to predict the overall effect of demographic attributes on the role modelling status.
6
Acculturation Impacts Obesity and CVD
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Data files were created in Microsoft Access software. After cleaning and processing, data were imported into Stata SE version 11.0 (StataCorp LP, College Station, TX) for analysis. In descriptive analyses, percentages means and 95% confidence intervals (CI) were calculated. Chi-squared test were conducted for categorical variables and analyses of variance was used for continuous variables. We used bivariate and multivariate models and for the logistic regression model we used a forward selection stepwise process to examine the association between socio-demographic, lifestyle and acculturation (indexed by years of residency) with the odds of overweight and obesity (based on BMI categories) and central obesity (waist-to-hip ratio). Alpha was set at 0.05. In our multivariable model we used age and income as continuous variable.
We also evaluated the effects of acculturation on other cardiovascular risk factors i.e. hypertension, diabetes and smoking. Participants who either self-reported doctor-diagnosed diabetes or who were taking medications that treat diabetes or their A1C level was greater or equal to 6.5 were classified as diabetes.
We also evaluated the effects of acculturation on other cardiovascular risk factors i.e. hypertension, diabetes and smoking. Participants who either self-reported doctor-diagnosed diabetes or who were taking medications that treat diabetes or their A1C level was greater or equal to 6.5 were classified as diabetes.
7
Predicting Lung Function Survival
8
Statistical Analysis Methods for Meta-analysis
9
Pooled Statistical Analysis Methodology
10
Prevalence and Determinants of CVD
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Statistic analyse were conducted using STATA SE version 11 (Stata Corp, College Station, TX). The prevalence of angina and stroke were calculated by using normalized weights in each country. Weights were based on selection probability, non-response, and post-stratification adjustments. To improve comparability across countries, the prevalence rates were age-standardized using the WHO World Standard Population Distribution based on world average population 2000-2025 [17 ]. Multivariate logistic regression was performed to examine the relationship between CVD and selected variables, including the socio-demographics such as age, sex, urban/rural setting, education, household wealth, and health risk factors such as smoking, alcohol drinking, fruit/vegetable intake, physical activity, hypertension and obesity. P < 0.05 from two-sided statistical tests was considered statistically significant.
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