Data were managed on Excel spreadsheets. Descriptive statistics was used to determine the frequency of symptoms, ECOG and KPS. Mean and SD were calculated for continuous variables. When the F-value indicated significant differences between group means, post hoc pairwise multiple comparisons were performed using Bonferroni method. Pearson's correlation coefficient was computed to assess relationship between two quantitative variables. P < 0.05 was considered as significant. All the statistical tests done in this study were two-tailed. STATA 11.0 version for Windows (STATA Corporation, College Station Road, Houston, Texas, USA) was used for data analysis.
Stata version 11.0 for windows
Manufactured by StataCorp
Sourced in United States
STATA version 11.0 for Windows is a statistical software package designed for data analysis, data management, and graphical presentation. It provides a comprehensive set of tools for researchers, analysts, and statisticians to conduct various types of statistical analyses. The software is available for the Windows operating system.
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Lab products found in correlation
15 protocols using stata version 11.0 for windows
1
Symptom Frequency and Performance Status Analysis
2
Symptom and Performance Status Analysis
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Data were managed on Excel Spreadsheets. Descriptive statistics were used to determine the frequency of symptoms and KPS. Mean and standard deviation (SD) were calculated for continuous variables. Comparison between two groups was done using Student's t- test. A P < 0.05 was considered statistically significant. All the statistical tests done in this study were two-tailed, and STATA 11.0 version for Windows (STATA Corporation, College Station Road, Houston, Texas, USA) was used for data analysis.
3
Omentin-1 Levels and Peripheral Artery Disease
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Demographic and clinical data of the groups were compared using a Chi squared test and a t-test. Omentin-1 serum levels were compared with a Mann–Whitney, Kruskal–Wallis and Dunn’s Multiple Comparison, when appropriate. A log transformation was applied to the not normally distributed variables (fasting glucose, glycated hemoglobin, triglyceride, and omentin-1 levels) prior to performing further analysis. A multivariate stepwise logistic regression analysis was performed, adjusted for traditional risk factors and omentin-1 levels. The area under the receiver operating characteristics (ROC) curve was calculated to test the predictive discrimination of PAD. All analyses were performed using STATA version 11.0 for Windows (Statistics/Data Analysis, Stata Corporation, College Station, TX, USA). Statistical significance was established at P < 0.05.
4
Epidemiology of E. coli in Chickens
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Data collected from questionnaire survey and laboratory study were entered in to Microsoft Excel () Spread sheet and analysed using STATA version 11.0 for windows (Stata corp. College Station, TX, USA). Descriptive statistics was utilized to summarize the data using percentages. The prevalence of E. coli with respect to district, sex, age, and season, health status, and diarrhoea were computed by dividing the number of positive chickens by the number of chickens examined and for organ level prevalence the number of positive organs was divided to the total number of organs examined. The association of potential risk factors with E. coli prevalence was analysed using logistic regression. Stratification method was used for those variables showing significant association to see any difference between the crude and adjusted results. Then, after further checking for collinearity, variables with P-value less than 0.25 during univariable analysis were further analysed using a multivariable logistic regression model. Odds ratio was used to see degree of association and confidence level was held at 95% and significance was at P < 0.05. The percentages of antimicrobial resistance of each pattern (Susceptible, Intermediate and Resistance) were calculated.
5
PPI Use and Colorectal Cancer Risk
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We compared the baseline characteristics of the study population according to the level of PPI exposure using a χ2 test. The primary analysis was a Cox proportional hazards analysis to estimate hazards ratios (HRs) and 95% CIs for the association between PPI use and CRC risk. We calculated the accumulated person-years of risk, beginning with the index date and ending with the date of CRC diagnosis, diagnosis of any other cancer, or death or December 31, 2013, whichever came first—CRC diagnosis was the primary endpoint.
We identified the relevant factors associated with CRC risk in our cohort. We also performed subgroup analyses stratified and combined by the CRC risk factors. In the stratified multivariable analyses, we reexamined the association between PPI use and the risk of CRC in different subgroups. We performed all analyses using STATA Version 11.0 for Windows (STATA Corp., TX). P-values <0.05 were considered to be statistically significant.
We identified the relevant factors associated with CRC risk in our cohort. We also performed subgroup analyses stratified and combined by the CRC risk factors. In the stratified multivariable analyses, we reexamined the association between PPI use and the risk of CRC in different subgroups. We performed all analyses using STATA Version 11.0 for Windows (STATA Corp., TX). P-values <0.05 were considered to be statistically significant.
6
Regression Analysis of Bivariate Data
7
Serological Survey of Foot-and-Mouth Disease
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The data collected from the questionnaire survey and laboratory investigations were entered into a Microsoft Excel spreadsheet (Microsoft Corporation) and analyzed using STATA version 11.0 for Windows (Stata Corp. College Station, TX, USA). The sum of seropositive samples divided by the overall number of samples tested was used to measure the seroprevalence. The association of seropositivity with the potential risk factors was computed by Pearson’s Chi-square test with a 95% confidence interval and a significance level at P≤0.05. The degree of association between FMD seroprevalence and categorical independent variables was assessed using odds ratio (OR) with multivariable logistic regression analyses. Before regression analysis, the data was checked for fulfillments of assumptions, such as the correlation of each variable (not more than 0.7), correlation of independent variables with the dependent variable (minimum of 0.3), and multi-collinearity tests (VIF (>10) and tolerance (>0.1)). And all tested variables did not show multi-collinearity. However, sex, breed, and herd size had a low correlation with the outcome variable (<0.3), thus decided to be omitted from the model. Statistical outputs were considered significant at p≤ 0.05.
8
Meta-analysis of Breviscapine Effects
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A meta-analysis was conducted using Stata version 11.0 for Windows (StataCorp LP, College Station, TX, USA). The principal measure of effect was the weighted mean difference (WMD) between the breviscapine and control groups, and the standardized mean difference (SMD) was used when analyzing 24-h urine protein as this is a continuous variable with large differences in mean. The confidence interval (CI) was 95%, as the outcome measurements were the same for each analysis. Heterogeneity was assessed using a χ2 test (P<0.1 was considered to indicate a statistically significant difference) and an I2 test (I2>50%, significant heterogeneity; I2<25%, insignificant heterogeneity). Begg's test was used to assess publication bias.
9
Statistical Analysis of Demographic Factors
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Data were coded, checked, and uploaded into Microsoft Excel 2010 spreadsheet computer program and analyzed using STATA version 11.0 for Windows (Stata Corp., College Station, USA). Univariate and binary logistic regression performed utilizing the same program for the first set of questions included sex, age, season, and origin. 95% confidence intervals were computed and a P value < 0.05 was considered statistically significant.
10
Predictors of Bacteriuria in Pregnant Women
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Data from laboratory investigation and questionnaire survey was entered into Microsoft Excel Spreadsheet. The coded data was processed and analyzed using STATA version 11.0 for Windows (Stata Corp., USA). Descriptive statistics was used to summarize the data.. Chi-square test was used to assess differences in the proportions of culture positive and negative participants. The prevalence of UTI was calculated. To determine predictors of bacteriuria, odds ratios were calculated using likelihood estimation technique. Independent variables (age, level of education, monthly income, parity, residence, raw meat consumption habit, raw milk consumption habit, washing habit and previous history of UTI) which are non-collinear and with P-values ≤0.25 in univariable logistic regression analysis were further tested via multivariable logistic regression in order to get adjusted odds ratios and significant predictors of UTI in pregnant women. P-value of <0.05 was considered statistically significant.
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