The data collected was checked for completeness, coded and entered into EpiData manager version 4.6 and exported to STATA version 14. Continuous variables were described using the mean and standard deviation. Categorical variables were expressed as frequencies and percentages and were compared using Chi-square tests. The correlation between AIP and cardiovascular risk factors was determined by Pearson chi-square correlation analyses. A p-value of less than 0.05 was considered statistically significant.
Epidata manager version 4
Manufactured by StataCorp
EpiData Manager version 4.6 is a data entry and documentation software application. It provides a user-friendly interface for creating and managing data entry forms, validating data, and generating data files in various formats.
Automatically generated - may contain errors
Lab products found in correlation
4 protocols using epidata manager version 4
1
Cardiovascular Risk and Arterial Intima-Media
2
Hematological Profiles in COVID-19 Patients
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The collected data was checked for completeness and consistency. The data was entered into the Epi-Data Manager version 4.6 and exported to STATA version 14.2 for statistical analysis. Necessary data processing, explorations, and data management like recoding, categorizing, merging, computing, and counting were done before the actual data analysis. Categorical variables were presented as frequencies with percentages, whereas mean, standard deviation, median, and range were used to describe continuous variables and displayed in graphs, charts, and tables. Evaluation of data normality was performed using the Shapiro-Wilk test. Non-normally distributed continuous variables were analyzed using non-parametric tests.22
Independent sample t-tests were applied to compare the hematological profiles’ across the wave of COVID-19 patients and COVID centers respectively. Categorical variables were compared using the Chi-square test or Fisher’s exact test. p ≤ 0.05 was considered as a statistically significant.
Independent sample t-tests were applied to compare the hematological profiles’ across the wave of COVID-19 patients and COVID centers respectively. Categorical variables were compared using the Chi-square test or Fisher’s exact test. p ≤ 0.05 was considered as a statistically significant.
3
Survival Analysis of Treatment Outcomes
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The data were coded and entered into Epi Data Manager version 4.6 and exported to Stata version 14 for cleaning, checking, and analysis. Age, weight, and height were further exported to Emergency Nutrition Assessment (ENA)-SMART software to calculate weight for height (WFH) %, weight for age (WFA) %, and height for age (HFA) % Z-scores. Descriptive statistics were presented with frequency tables and graphs for the categorical variables and the continuous variables were reported with means [mean ± standard deviation (SD)] and medians [interquartile range (IQR)]. The Kaplan–Meier survival curve was used to estimate the median survival time and to identify the presence of a difference in recovery time/length of hospitalization among categorical variables. The Cox proportional hazard model assumption was checked using the Schoenfeld residuals test, Cox–Snell residual, and parallel assumption test. The association between the independent variables and the outcome variable was assessed by the Cox proportional hazard model. Variables with a p-value <0.2 in the bivariable model were a candidate for multivariable analysis. A 95% CI of the adjusted hazard ratio (AHR) was computed and variables with a p-value <0.05 in the multivariable model were considered statistically significant on the dependent variables.
4
Contraceptive Use and Socio-demographic Factors
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Data were coded, entered into Epi Data Manager (version 4.6)22
and exported to Stata version 1523
for cleaning and analysis. The dependent dichotomous variable was the use of any contraceptive method currently. The use of contraceptive methods as well as other socio-demographic characteristics was summarized using descriptive statistics. In estimating the influence of socio-demographic characteristics on the use of contraceptives, we stratified participants by age categories, 18–24 years and 25–49 years. A Pearson’s chi-square test was used to determine the association between socio-demographic characteristics and the use of contraceptives. Statistically significant variables in the bivariate analysis were used as predictors in the multivariate logistic regression. The results were expressed as odds ratio (OR) with their 95% confidence interval (CI) and statistical significance level of p < 0.05.
and exported to Stata version 1523
for cleaning and analysis. The dependent dichotomous variable was the use of any contraceptive method currently. The use of contraceptive methods as well as other socio-demographic characteristics was summarized using descriptive statistics. In estimating the influence of socio-demographic characteristics on the use of contraceptives, we stratified participants by age categories, 18–24 years and 25–49 years. A Pearson’s chi-square test was used to determine the association between socio-demographic characteristics and the use of contraceptives. Statistically significant variables in the bivariate analysis were used as predictors in the multivariate logistic regression. The results were expressed as odds ratio (OR) with their 95% confidence interval (CI) and statistical significance level of p < 0.05.
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