The questionnaire contents were input as statistical data using Epi data software, and SPSS 26.0 was used for statistical analysis of the obtained data. Basic descriptive data were represented by examples and constituent ratios; measurement data that conform to normal distribution were represented by mean ± standard deviation; measurement data that did not conform to normal distribution were represented by interquartile range, and nonparametric test analysis was used; enumeration data were represented by Chi-square test and was used to conduct one-way ANOVA; multivariate logistic regression analysis was used to analyze the correlation between each factor and HCV infection. P < 0.05 was considered statistically significant.
Epi data software
Manufactured by IBM
Sourced in United States, Denmark
Epi-data software is a laboratory data management and analysis tool developed by IBM. It provides functionality for organizing, storing, and analyzing data generated from various laboratory equipment and experiments. The software is designed to facilitate efficient data management and reporting within a laboratory environment.
Automatically generated - may contain errors
Lab products found in correlation
Statistical package for social science version 20, by IBM (2 mentions)
Spss statistics version 25, by IBM (1 mentions)
Statistical package for social sciences software, by IBM (1 mentions)
Spss statistics for windows version 26, by IBM (1 mentions)
Spss statistics for macintosh, by IBM (1 mentions)
Spss software version 20, by IBM (1 mentions)
Spss 21, by IBM (1 mentions)
13 protocols using epi data software
1
HCV Infection Risk Factors Analysis
2
Diagnostic Risk Score Calculation
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Patient data were stored in a computer database using EpiData software; Statistical data processing and figure generation were conducted using SPSS Statistics, version 25.0 (SPSS Inc. Chicago, IL, USA)and GraphPad Prism, version 7.0 (GraphPad Software Inc.).
The DRS for each sample was calculated as described by Kaforou et al.6 (link) Individual DRS values were first obtained using normalized values (∆Ct). Thereafter, the scale of ∆Ct values was increased 10‐fold to avoid negative values when logarithmic transformation was performed. The final DRS formula was log2(FAM89A expression) – log2(IFI44L expression).
Normally distributed data were presented as (mean ± standard deviation), whereas data with skewed distributions were presented as median (interquartile range). Statistical analysis was performed using the t‐test, χ2 analysis, and the nonparametric rank‐sum test. Receiver operator characteristic (ROC) curves were generated using GraphPad Prism, version 7.0; areas under the ROC curve (AUC) were compared among experiments using the Z test, while joint predictors were analyzed using a logistic regression model. P <0.05 was considered statistically significant.
The DRS for each sample was calculated as described by Kaforou et al.6 (link) Individual DRS values were first obtained using normalized values (∆Ct). Thereafter, the scale of ∆Ct values was increased 10‐fold to avoid negative values when logarithmic transformation was performed. The final DRS formula was log2(FAM89A expression) – log2(IFI44L expression).
Normally distributed data were presented as (mean ± standard deviation), whereas data with skewed distributions were presented as median (interquartile range). Statistical analysis was performed using the t‐test, χ2 analysis, and the nonparametric rank‐sum test. Receiver operator characteristic (ROC) curves were generated using GraphPad Prism, version 7.0; areas under the ROC curve (AUC) were compared among experiments using the Z test, while joint predictors were analyzed using a logistic regression model. P <0.05 was considered statistically significant.
3
Puerperal Infection Data Analysis
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Clinical relevant data of subjects with puerperal infection were analyzed, and all clinical data were collected by obstetricians and related investigators receiving unified training. Patients and their authorizers should be fully cooperated in the collection of investigation data, the investigation was conducted anonymously, the patients' privacy should be protected during the process, and the question raised by patients was answered. The relevant investigation results obtained should not be disclosed to any organization or individual without the permission of subjects enrolled and their authorizers. At enrollment and 30 days after the first investigation, investigators filled out the same questionnaire and completed the data of subjects, and the correlation coefficients obtained in the two times were set as the stability coefficients. In this study, the reliability coefficient (α = 0‐1) was used to evaluate the reliability level, and it was 0.921 after calculation. Clinical data obtained were checked alternatively by two people and entered into the EpiData software data analysis system, followed by statistical processing via Statistical Product and Service Solutions (SPSS) 21.0.
4
Percutaneous Injury Prevalence Analysis
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Accuracy of data was checked timely, and data cleaning was made before analysis. Collected data was entered into epi-data software, exported to Statistical Package for the Social Sciences (SPSS) version 16, cleaned and analyzed. Percutaneous injury is dichotomized in two ways before analysis, ever and one year percutaneous injury. The dichotomization was done to simplify analysis and interpretation of the results. Association between dependent and independent variables was examined using bivariate and multivariate logistic regression models and reported as unadjusted odds ratio (OR) and adjusted odds ratios (AOR) with 95 % confidence interval (CI). P-value was set at less than 0.05 to verify existence of association. In order to avoid an excessive numbers of variables and unstable estimates, only variables that reached a P-value less than 0.25 were included in the subsequent analysis (multivariate logistic regression analysis model).
5
Statistical analysis
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All information was input and verified using EpiData software, and data processing and analysis were performed using SPSS 13.0 software (SPSS, Inc., Chicago, IL, USA). Measurement data were expressed as the mean ± standard deviation. Count data were expressed as percentages and differences between the groups were compared using the χ2 test. A P value of <0.05 was defined as statistically significant.
6
Prevalence and Antimicrobial Susceptibility of Nosocomial Infections
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Data were entered into the EpiData software (v. 2; Odense, Denmark) and analyzed using the Statistical Package for Social Sciences software (v. 22; SPSS, Inc, Illinois, USA). The prevalence of NIs was presented in percentage along with the 95% confidence interval (CI). Two or more bacteria isolated from one patient were categorized as one bacterium for summarizing the prevalence but were analyzed separately for the antimicrobial susceptibility profile. Each bacterium was tested in triplicate for a single antimicrobial, and the mean value was taken to determine its antimicrobial susceptibility pattern. Intermediate results were included in the resistant category for analysis.
7
Attitudes Towards Professional Help
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Epi-data software and Statistical Package of Social Science Software program (SPSS) version 26.0 software (IBM SPSS Statistics for Windows, Version 26.0. Armonk, NY: IBM Corp) were used for data entry and analysis of data respectively. Mean and standard deviation were calculated in descriptive statistics. Inferential statistical analysis used determine the association of attitude toward professional help and social-demographic characteristics. Statistical significance was determined by p-value by <0.05.
8
Factors Associated with Albuminuria
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Collected data was checked for completeness by principal investigator. Serum creatinine based glomerular filtration rate was estimated for all study participants by CG, MDRD-4 and CKD-EPI equations using QxMD calculator with correction for black race. Urinalysis of all study subjects were revised; after excluding possible causes of functional albuminuria, dipstick proteinuria of > = + 1 was taken as albuminuria. Finally, the data was entered in to the computer using EpiData software and after verification; it was exported to SPSS (IBM SPSS Statistics for Macintosh, Version 20.0. Armonk, NY) for analysis. Descriptive statistics like percentages, means, medians, standard deviations and ranges were used to describe findings.
A bi-variate analysis was done to sort variables candidate for multiple logistic regression having value less than or equals to 0.25. Multiple logistic regression analyses were conducted using Backward LR to generate factors associated with the dependent variable. P-value < 0.05 and 95% confidence interval (CI) and AOR was used in judging the statistical significance of the associations between independent variables and the outcome variable.
A bi-variate analysis was done to sort variables candidate for multiple logistic regression having value less than or equals to 0.25. Multiple logistic regression analyses were conducted using Backward LR to generate factors associated with the dependent variable. P-value < 0.05 and 95% confidence interval (CI) and AOR was used in judging the statistical significance of the associations between independent variables and the outcome variable.
9
Eosinophil Count Analysis in Patients
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The data was entered into Epi-data software (Version: 3.0.4), cleaned, and exported into Statistical Package for Social Science version 20 software (IBM Corp., Armonk, NY, USA) for analysis. To summarize the data, descriptive statistics such as frequencies and percentages were used. The Shapiro-Wilk test and histogram were used to check the normal distribution of continuous variables. The data was presented using tables.
Non-parametric (Mann-Whitney test) was used to compare median values of absolute eosinophil count with different background variables. Binary logistic regression, such as bivariable and multivariable logistic regression analysis, was performed. The strength of association between predictors and outcome was determined using the crude odds ratio (COR) and adjusted odds ratio (AOR) with a 95% confidence interval (CI). In the bivariable logistic regression analysis, variables having a p-value of less than 0.25 were fitted into the multivariable logistic regression analysis. Hosmer and Lemeshow’s goodness of fit statistics were used to test the model’s fitness. In all cases, a p-value of less than 0.05 was considered statistically significant.
Non-parametric (Mann-Whitney test) was used to compare median values of absolute eosinophil count with different background variables. Binary logistic regression, such as bivariable and multivariable logistic regression analysis, was performed. The strength of association between predictors and outcome was determined using the crude odds ratio (COR) and adjusted odds ratio (AOR) with a 95% confidence interval (CI). In the bivariable logistic regression analysis, variables having a p-value of less than 0.25 were fitted into the multivariable logistic regression analysis. Hosmer and Lemeshow’s goodness of fit statistics were used to test the model’s fitness. In all cases, a p-value of less than 0.05 was considered statistically significant.
10
Survival Analysis of MDR-TB Patients
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Data were entered into an access database using Epidata software (Version 3.1, Odense, Denmark), cleaned and analyzed using the statistical package for the social sciences (SPSS, version 24.0, IBM SPSS, IBM Corp, Armonk, NY, USA). Kaplan-Meier analysis and the log-rank test were used to assess the differences in survival among patients. Cox regression model was used for multivariate analysis. In the single factor analysis of MDR-TB, the qualitative data was tested with chi-square test, multivariate analysis was performed using binary logistic regression analysis, and the significant level for all the tests was set at 0.05.
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